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Snipers, automatic weapons, armored vehicles, and sonic weapons were just some of the military equipment aimed at the people of Ferguson, Missouri by police for protesting the shooting death of Michael Brown by a Ferguson police officer. Brown was said to have been shot and killed by the officer after putting his hands up in the air.
Protests in response to the shooting of Brown were met with military equipment and occupation tactics – including the arrest of two journalists reporting from the scene who were later released.
The police adorned themselves in combat rather than riot gear and were seen multiple times pointing deadly weapons at protesters.
This photo from the Times really is incredible http://t.co/3uikNM6CNB h/t @conor64 pic.twitter.com/RdcmtxFLP5
— Adam Serwer (@AdamSerwer) August 12, 2014
As night fell police demanded the protesters disperse and when they did not began using sonic weapons (LRAD), tear gas, flash bombs and rubber bullets. The dispersal tactics combined with the armored vehicles and combat gear makes it seem like Ferguson, Missouri is a war zone. Which is what happens when police are militarized.
According to The New York Times, Since 2006, police have acquired at least 867 armored vehicles, 533 aircraft and 93,763 machine guns due to US military surplus. Combine military hardware and an ideology of war on terrorism, drugs and broken windows and suddenly 99% of the public are enemy combatants.
Will this soon be every town's future?
Incredible photo via @AP_Images: "A device deployed by police goes off in the street as police and protesters clash" pic.twitter.com/5FUGo85sZd
— J. Freedom du Lac (@jfdulac) August 14, 2014


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During a Vancouver stop on his Friends, Fans and Family Tour, Waka Flocka met up with Canadian interview jewel Nardwuar. No spoilers for this one because Flocka's unfettered excitement over Nardwuar's C.I.A.-esque research skills makes this one of Nardwuar's best interviews to date.
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Starkville High School football coach Jamie Mitchell knew his team was going to have to score points to defeat the Noxubee County Tigers Friday night.
Eight was not enough.
The Yellowjackets were dominated on both sides of the football in their home stadium and lost a 29-8 decision to open the season.
MABEN – Adam Lowrey took last week's 7-6 loss to East Webster in a jamboree pretty seriously.
So Monday he decided to change some things up.
It was evident it West Oktibbeha's 66-6 victory over S.V. Marshall Friday night at the West Oktibbeha High School football field.
VARDAMAN – The East Webster Wolverines scored four touchdowns on the ground and added a field goal to beat Vardaman 30-14 Friday night.
Deangelo Liggins scored from 28-yards out in the first quarter to make it 7-0. Tyler Crosby converted the point after touchdown.
Marques Reives scored from 8 yards out to make it 14-0 in the first quarter. Crosby, again, was good on the extra point.
The Mississippi State Bulldogs defeated Southeastern Louisiana 2-1 at the MSU Soccer Stadium in its season opener Friday night.
With State starting the season off 0-3 and finishing at 6-10-3 last year, the team wanted to get off to a fast start with a victory this year.
It only took the Bulldogs' first game to score two goals and obtain a victory which took four games last year.
East Oktibbeha's season-opening football game at Leake Central, originally scheduled for Friday night at 7:30 p.m. has been moved to Saturday at 3 p.m. in Carthage.
The reason for the change was lighting issues at Leake Central.
Starkville High School football coach Jamie Mitchell has been in search of a center and long snapper during the preseason.
Going into tonight's game, Mitchell believes he has found two players in Jalen Henderson and Chris Rogers who can handle the task.
The Starkville Academy Volunteers look to build on the momentum of a successful 2011 football season.
The Volunteers made an appearance in the Class AAA, Division II State championship game, but lost to Washington School.
SA opens the season on the road at St. Andrew's tonight at 7:30 p.m.
The East Webster Wolverines may be playing a Class A squad, but it will be no pushover.
The Wolverines open the season on the road at Vardaman tonight at 7:30 p.m.
"We know we have a very tough opponent," East Webster head coach Doug Wilson said. "Vardaman is one of the No. 1 teams in 1A. They have a great coaching staff, a good team and going to be a very tough game for us."
The Starkville High School girls' volleyball team learned early in the year what it took to be a successful team.
That was witnessed by those who attended Thursday night's match in the Beehive when the Lady Yellowjackets beat division foe Ridgeland 3-0.
"It's not whether you win or lose. It's how you play the game."
That old saying is more true at certain times than others.
When it comes to the 10th annual College Exposure Softball Tournament at the Starkville Sportsplex Saturday, it's important for the players to put on a good show.


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Originally written on Fox Sports West | Last updated 1/5/13
LOS ANGELES It's not about the Lakers anymore. There might still be a rivalry a battle to stake a claim to LA but the Clippers look at themselves from a different angle, a different standard. They beat the Lakers 107-102 Friday night at Staples Center and ended a two-game losing streak. The fact it was their co-tenant they beat seemed less important than proving they could shake the malaise that kept them winless in the new year. "Right now, we're focused on being one of the best teams in the league, not just the city," Lamar Odom said. "The focus can't be just to be better than one team. That's a small way of thinking. We're trying to be great." It's a worthy goal for a team that has the second-best record in the NBA and leads the Lakers by 10 games in the Pacific Division. And while Lakers fans point to all those championship banners draped along the wall at Staples, the Clippers at least can appreciate the fact they're finally a credible opponent. So no, they don't mind th...
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text: We have eaten at Le Bistro on a number of occasions over the years, and have left this place because the menu became repetitive and the quality diminished over time. However, about 6 weeks ago, we decided to give Le Bistro another chance. After all, it had been a long, long time..... Just to say... read more


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Tue March 12, 2013
Arizona State's Mascot Gets A Makeover, And Backlash
By editor
Good morning. I'm Steve Inskeep. Arizona State never had much luck with sports mascots. When it was a normal, or a teacher's school, they were the Normals. Later they became the Arizona State Sun Devils. This mascot is Sparky the Sun Devil in a red costume with horns and a pitchfork. Now Disney has helped update Sparky, but some students find his big eyes and bulging muscles creepy. Students may vote on Sparky's future after the campus paper warned he'll scare kids. It's MORNING EDITION. Transcript provided by NPR, Copyright NPR.


text:
Narrow your search
Radar-guided cruise control means it's technically possible to drive through town after pressing the 'go' pedal once and once only. So, naturally, we had a go in London and hoped there'd be no idiots on the road.
Batman: Arkham Knight will have no loading screens and will feature a robust New Game Plus mode, developers say.
The Super Pit is the largest open-pit gold mine in Australia, and one of the largest in the world. House-size machines dig into a canyon-size hole. Here's a full tour.
The OXO Good Grips Cold Brew Coffee Maker offers a simple way to go from grind to cup. The device steeps coffee and then releases it into a waiting beaker.
Apple's been gaining more partners since it announced the service last month and it's ready to take it live.
The machine, still in testing, will sell 10-inch pizzas straight out of an 800-degree oven. In 90 seconds.
Apple will help consumers say buh-bye to plastic credit cards with the NFC-enabled iPhone 6, iPhone 6 Plus, and Apple Watch using its new mobile payment service Apple Pay.
Burger King offers a free Android smartphone with your chicken fries, but the fine print shows it's business as usual. Crave's Amanda Kooser digs in.
This mammoth Samsung drive features auto-backup software and a three-year warranty.
Go back to the future every time you climb into your car with a USB car charger designed to look like the glowing creation from Doc Brown's DeLorean.


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Flow Free: Bridges 7x7 level 58 tips
Here's a guide of 7x7 level 58 for the iOS app Flow Free: Bridges. Have you stuck in Flow Free: Bridges 7x7 level 58? Watch this video which will guide you through from the beginning to the end with strategies, hints and tips for this level.
This tutorial it's a part of a complete Flow Free: Bridges level walkthroughs that were submitted by players for Flow Free: Bridges game on iphone, iPad or Android.Flow Free: Bridges level 58 video:
If you have any other information on how to pass Flow Free: Bridges 7x7 level 58 - please post your tactics here. You can also find help with other levels in the Flow Free: Bridges walkthrough for all levels here.


text:
Elk River Municipal Utilities has announced the winners of its Holiday Lighting Contest. •Grand prize: 15726 96th Circle, Otsego •First runner up: 925 York Ave., Elk River •Second runner up: 19676 Hudson Circle, Elk River •Most creative: 1839 Main St., Elk River •Best use of LED lighting: ...Read More
Elk River Municipal Utilities
Do you like to put up big displays of holiday lighting outside your home? If so, you're a perfect candidate for Elk River Municipal Utilities' holiday lighting contest. You must be within ERMU's electric service territory to be eligible for the contest. Applications can be accessed at the ERMU...Read More
by Joni Astrup Associate Editor The Salvation Army Family Store in Elk River has converted to energy-efficient LED lighting, which will save nearly $10,000 a year in electrical costs. Located in a former grocery store at 321 Baldwin Ave., the Salvation Army Family Store had been illuminated by 59-wa...Read More
Elk River Municipal Utilities is sponsoring a residential holiday lighting contest. Participants must be in ERMU's electric service territory to be eligible. The application form is available at www.elkriverutilities.com and must be completed and returned to the ERMU office at 13069 Orono Parkway ...Read More
• Utility companies launch effort to raise customer awareness by Jim Boyle Editor For the first time, 13 Minnesota utilities are joining forces to fight back against scams. Reports of phone and email billing scams targeting Minnesota utility customers are increasing at an alarming rate, represent...Read More
The Elk River City Council has recognized Oct. 5-11 as Public Power Week. Elk River Municipal Utilities, in its 67th year operating as a municipally owned public power utility, and in its 99th year operating since incorporation, is also celebrating the week. This national event is sponsored in con...Read More
Elk River Municipal Utilities has received over a 95 percent customer satisfaction rating in a recent survey of its customers. Some of the key areas evaluated on were: respectfulness, communication, knowledge, innovation and timeliness. One of the main reasons ERMU conducts the survey is to find o...Read More
ERMU recognized for reliable operations Elk River Municipal Utilities is one of 184 of the nation's more than 2,000 public power utilities to earn the Reliable Public Provider (RP3) designation for providing customers with the highest degree of reliable and safe electric service. The RP3 designati...Read More
The prolonged and severely cold winter has increased the impact frost is having on both public and private storm sewer, sanitary sewer, and water lines, causing problems with frozen lines. Standing or rising water in the lowest street drain is a sign of a frozen storm sewer or catchbasin/drain. Call...Read More
Elk River is experiencing some frozen water lines this winter. Elk River Municipal Utilites has had 15 frozen water lines so far this year; it typically has one or two. Water lines are about 7 feet underground. Right now, the frost is hovering around 6.5-7.5 feet. All residents should be monitoring ...Read More
Elk River Municipal Utilities has announced the winners of its Holiday Lighting Contest. •First Place: 15726 96th Circle, Otsego •Runner-Up: 925 York Ave., Elk River •Second Runner-Up: 18209 Hudson Lane, Elk River •Most Creative: 18680 Smith Drive, Elk River •Best Use of LED Lighting: 1863...Read More


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Nice story about the 20-year old that caught Leake and Cozart's back-to-back HRs at yesterday's game. He gave Leake's back to the Reds.
Reds fan catches two home runs in same inning and then gives both balls away
From MLB.com:
"I gave the Cozart one to my buddy since his uncle got us the tickets," Lloyd said. "He was the reason I was here. The Leake ball, I gave it back, because I knew it was Leake's first Major League home run. I just want to meet him and shake his hand."


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A nuclear Iran
As we enter a new year, speculation continues over Iran's nuclear program and how Israel will act in response to its continued development.
While a nuclear Iran is a frightening thought, leading commentator Hugh White believes it's inevitable, and that we must learn how to live with it.
The professor of strategic studies at the ANU and visiting fellow at the Lowy Institute backgrounds his controversial point of view.
This interview was first broadcast on January 28, 2012
- Hugh White
- Professor of Strategic Studies at the ANU
- Presenter
- Geraldine Doogue
- Producer
- Kathy Gollan


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|Residence Inn By Marriott Southgate hotels are provided below. Search for cheap and discount Residence Inn By Marriott hotel prices in Southgate, CA for your family, individual or group travels. We list the best Residence Inn By Marriott Southgate venues so you can review the Southgate Residence Inn By Marriott hotel list below to find the perfect place.
This site will list Southgate, CA hotels & motels that have a comparable rating of a Residence Inn By Marriott.
Need a monthly rate? Get long term quotes from hotels in Southgate CA
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Changes Counseling, LLC offers Free Decpression Screening
October 01, 2014
(Mendota, Illinois) – Changes Counseling, LLC is proud to participate in the 23rd annual National Depression Screening Day® by providing free screenings for depression and other mood disorders at Mendota Community Hospital on October 8 from 2:00-4:00 and October 9th from 3:30-5:30. National Depression Screening Day raises awareness and screens people for depression and mood disorders and allows individuals to identify warning signs and connect with the appropriate treatment resources. Similar to many physical illnesses, early recognition and treatment offers the best opportunity for recovery from mental illness.
Childhood Obesity: A "GROWING" Problem
September 05, 2014
A number of years ago it was felt that that when a child was "chunky" they would grow out of it as they grew taller. That is not true anymore. Childhood obesity has doubled in children and quadrupled in adolescents in the past 30 years. According to the Center for Disease Control one third of children and adolescents are overweight or obese. This is a result of caloric imbalance: too many calories taken in and too few calories expended (physical activity). The old saying "calories in vs. calories out" still applies.
Back to School Checklist
August 13, 2014
Pencils check. Pens check. Paper check. Immunizations ….. Do we really need them? The answer is YES WE DO!!!! Immunizations can prevent serious diseases like the flu, measles, and chicken pox. It's important to know which shots you need and when to get them. August is an ideal time to make sure everyone is up-to-date on vaccines before heading back to school.
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The present invention involves a Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a plant such as, for example, an internal combustion engine or an automobile suspension system. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and/or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.
1. A method for optimizing a knowledge base in a soft computing controller, comprising:
selecting a fuzzy model by selecting one or more parameters, said one or more parameters comprising at least one of a number of input variables, a number of output variables, a type of fuzzy inference model, and a teaching signal;
optimizing linguistic variable parameters of a knowledge base according to said one or more parameters to produce optimized linguistic variables;
ranking rules in said rule base according to firing strength;
eliminating rules with relatively weak firing strength leaving selected rules from said rules in said rule base; and
optimizing said selected rules, using said fuzzy model, said linguistic variable parameters and said optimized linguistic variables, to produce optimized selected rules.
2. The method of
3. The method of
This present application claims priority benefit of U.S. Provisional Application No. 60/490,397, filed Jul. 25, 2003, titled "SOFT COMPUTING OPTIMIZER OF INTELLIGENT CONTROL SYSTEM STRUCTURES," the entire contents of which is hereby incorporated by reference.
1. Field of the Invention
The present invention relates generally to control systems, and more particularly to the design method of intelligent control system structures based on soft computing optimization.
2. Description of the Related Art
Feedback control systems are widely used to maintain the output of a dynamic system at a desired value in spite of external disturbances that would displace it from the desired value. For example, a household space-heating furnace, controlled by a thermostat, is an example of a feedback control system. The thermostat continuously measures the air temperature inside the house, and when the temperature falls below a desired minimum temperature the thermostat turns the furnace on. When the interior temperature reaches the desired minimum temperature, the thermostat turns the furnace off. The thermostat-furnace system maintains the household temperature at a substantially constant value in spite of external disturbances such as a drop in the outside temperature. Similar types of feedback controls are used in many applications.
A central component in a feedback control system is a controlled object, a machine or a process that can be defined as a "plant", whose output variable is to be controlled. In the above example, the "plant" is the house, the output variable is the interior air temperature in the house and the disturbance is the flow of heat (dispersion) through the walls of the house. The plant is controlled by a control system. In the above example, the control system is the thermostat in combination with the furnace. The thermostat-furnace system uses simple on-off feedback control proportional feedback control, integral feedback control, and derivative feedback control. A feedback control based on a sum of proportional, plus integral, plus derivative feedbacks, is often referred as a P(I)D control.
A P(I)D control system is a linear control system that is based on a dynamic model of the plant. In classical control systems, a linear dynamic model is obtained in the form of dynamic equations, usually ordinary differential equations. The plant is assumed to be relatively linear, time invariant, and stable. However, many real-world plants are time varying, highly non-linear, and unstable. For example, the dynamic model may contain parameters (e.g., masses, inductance, aerodynamics coefficients, etc.), which are either only approximately known or depend on a changing environment. If the parameter variation is small and the dynamic model is stable, then the P(I)D controller may be satisfactory. However, if the parameter variation is large or if the dynamic model is unstable, then it is common to add Adaptive or Intelligent (AI) control functions to the P(I)D control system.
AI control systems use an optimizer, typically a non-linear optimizer, to program the operation of the P(I)D controller and thereby improve the overall operation of the control system.
Classical advanced control theory is based on the assumption that all controlled "plants" can be approximated as linear systems near equilibrium points. Unfortunately, this assumption is rarely true in the real world. Most plants are highly nonlinear, and often do not have simple control algorithms. In order to meet these needs for a nonlinear control, systems have been developed that use Soft Computing (SC) concepts such Fuzzy Neural Networks (FNN), Fuzzy Controllers (FC), and the like. By these techniques, the control system evolves (changes) in time to adapt itself to changes that may occur in the controlled "plant" and/or in the operating environment.
Control systems based on SC typically use a Knowledge Base (KB) to contain the knowledge of the FC system. The KB typically has many rules that describe how the SC determines control parameters during operation. Thus, the performance of an SC controller depends on the quality of the KB and the knowledge represented by the KB. Increasing the number of rules in the KB generally increases (very often with redundancy) the knowledge represented by the KB but at a cost of more storage and more computational complexity. Thus, design of a SC system typically involves tradeoffs regarding the size of the KB, the number of rules, the types of rules. etc. Unfortunately, the prior art methods for selecting KB parameters such as the number and types of rules are based on ad hoc procedures using intuition and trial-and-error approaches.
The present invention solves these and other problems by providing a SC optimizer for designing a KB to be used in a SC system such as a SC control system. In one embodiment, the SC optimizer includes a fuzzy inference engine. In one embodiment, the fuzzy inference engine includes a Fuzzy Neural Network (FNN). In one embodiment, the SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and Teaching signal selection.
In one embodiment, the user makes the selection of fuzzy model, including one or more of: the number of input and/or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions.
In one embodiment, a Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. In one embodiment, a GA is used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal.
One embodiment, includes fine tuning of the FNN. The GA produces a near-optimal FNN. In one embodiment, the near-optimal FNN can be improved using classical derivative-based optimization procedures.
One embodiment, includes optimization of the FIS structure by using a GA with a fitness function based on a response of the actual plant model.
One embodiment, includes optimization of the FIS structure by a GA with a fitness function based on a response of the actual plant.
The result is a specification of an FIS structure that specifies parameters of the optimal FC according to desired requirements.
Using a set of inputs, a fitness function 132 in a GA 131 works in a manner similar to an evolutionary process to arrive at a solution which is, hopefully, optimal. The GA 131 generates sets of "chromosomes" (that is, possible solutions) and then sorts the chromosomes by evaluating each solution using the fitness function 132. The fitness function 132 determines where each solution ranks on a fitness scale. Chromosomes (solutions) which are more fit, are those which correspond to solutions that rate high on the fitness scale. Chromosomes which are less fit, are those which correspond to solutions that rate low on the fitness scale.
Chromosomes that are more fit are kept (survive) and chromosomes that are less fit are discarded (die). New chromosomes are created to replace the discarded chromosomes. The new chromosomes are created by crossing pieces of existing chromosomes and by introducing mutations.
A P(I)D controller 150 has a substantially linear transfer function and thus is based upon a linearized equation of motion for the controlled "plant" 120. Prior art GA used to program P(I)D controllers typically use simple fitness functions and thus do not solve the problem of poor controllability typically seen in linearization models. As is the case with most optimizers, the success or failure of the optimization often ultimately depends on the selection of the performance (fitness) function 132.
Evaluating the motion characteristics of a nonlinear plant is often difficult, in part due to the lack of a general analysis method. Conventionally, when controlling a plant with nonlinear motion characteristics, it is common to find certain equilibrium points of the plant and the motion characteristics of the plant are linearized in a vicinity near an equilibrium point. Control is then based on evaluating the pseudo (linearized) motion characteristics near the equilibrium point. This technique is scarcely, if at all, effective for plants described by models that are unstable or dissipative.
Computation of optimal control based on SC includes the GA 131 as the first step of global search for optimal solution on a fixed space of positive solutions. The GA searches for a set of control weights for the plant. Firstly the weight vector K={k1, . . . , kn} is used by a conventional proportional-integral-differential (P(I)D) controller 150 in the generation of a signal δ(K) which is applied to the plant. The entropy S(δ(K)) associated to the behavior of the plant on this signal is assumed as a fitness function to minimize. The GA is repeated several times at regular time intervals in order to produce a set of weight vectors. The vectors generated by the GA 131 are then provided to a FNN 142 and the output of the FNN 142 to a Fuzzy Controller (FC) 143. The output of the FC 143 is a collection of gain schedules for the P(I)D-controller 150 that controls the plant.
The SSCQ 130 finds teaching patterns (input-output pairs) for optimal control by using the GA 131 based on a mathematical model of controlled plant 120 and physical criteria of minimum of entropy production rate. The FLCS 240 produces an approximation of the optimal control produces by the SSCQ 130 by programming the optimal FC 243.
The SSCQ 130 provides acquisition of a robust teaching signal for optimal control. The output of SSCQ 130 is the robust teaching signal, which contains the necessary information about the optimal behavior of the plant 120 and corresponding behavior of the control system 200.
The SC optimizer 242 produces an approximation of the teaching signal by building a Fuzzy Inference System (FIS). The output of the SC optimizer 242 includes a Knowledge Base (KB) for the optimal FC 243.
The optimal FC operates using an optimal KB from the FC 243 including, but not limited to, the number of input-output membership functions, the shapes and parameters of the membership functions, and a set of optimal fuzzy rules based on the membership functions.
In one embodiment the optimal FC 243 is obtained using a FNN trained using a training method, such as, for example, the error back propagation algorithm. The error back propagation algorithm is based on application of the gradient descent method to the structure of the FNN. The error is calculated as a difference between the desired output of the FNN and an actual output of the FNN. Then the error is "back propagated" through the layers of the FNN, and the parameters of each neuron of each layer are modified towards the direction of the minimum of the propagated error. The back propagation algorithm has a few disadvantages. First, in order to apply the back propagation approach it is necessary to know the complete structure of the FNN prior to the optimization. The back propagation algorithm can not be applied to a network with an unknown number of layers or an unknown number nodes. Second, the back propagation process cannot modify the types of the membership functions. Finally, the back propagation algorithm very often finds only a local optimum close to the initial state rather then the desired global minimum. This occurs because the initial coefficients for the back propagation algorithm are usually generated randomly. The error back propagation algorithm is used, in a commercially available Adaptive Fuzzy Modeler (AFM). The AFM permits creation of Sugeno 0 order FIS from digital input-output data using the error back propagation algorithm. The algorithm of the AFM has two steps. In the first AFM step, a user specifies the parameters of a future FNN. Parameters include the number of inputs and number of outputs and the number of fuzzy sets for each input/output. Then AFM "optimizes" the rule base, using a so-called "let the best rule win" (LBRW) technique. During this phase, the membership functions are fixed as uniformly distributed among the universe of discourse, and the AFM calculates the firing strength of the each rule, eliminating the rules with zero firing strength, and adjusting centers of the consequents of the rules with nonzero firing strength. It is possible during optimization of the rule base to specify the learning rate parameter. The AFM also includes an option to build the rule base manually. In this case, user can specify the centroids of the input fuzzy sets, and then the system builds the rule base according to the specified centroids.
In the second AFM step, the AFM builds the membership functions. The user can specify the shape factors of the input membership functions. Shape factor supported by the AFM include: Gaussian; Isosceles Triangular; and Scalene Triangular. The user must also specify the type of fuzzy AND operation in the Sugeno model, either as a product or a minimum.
After specification of the membership function shape and Sugeno inference method, AFM starts optimization of the membership function shapes. The user can also specify optional parameters to control optimization rate such as a target error and the number of iterations.
AFM inherits the limitations and weaknesses of the back propagation algorithm described above. The user must specify the types of membership functions, the number of membership functions for each linguistic variable and so on. AFM uses rule number optimization before membership functions optimization, and as a result, the system becomes very often unstable during the membership function optimization phase.
The Structure of an Intelligent Control System Including SC-Optimizer
In one embodiment, the SC optimizer 242 includes (as shown in
In one embodiment, output from the SC optimizer 242 can be exported to other programs or systems for simulation or actual control of a plant 130. For example, output from the FC optimizer 242 can be exported to a simulation program for simulation of plant dynamic responses, to an online controller (to use in control of a real plant), etc.
The Structure of the SC Optimizer
In Stage 1, the user selects a fuzzy model by selecting one or parameters such as, for example, the number of input and output variables, the type of fuzzy inference model (Mamdani, Sugeno, Tsukamoto, etc.), and the source of the teaching signal.
In Stage 2, a first GA (GA1) optimizes linguistic variable parameters, using the information obtained in Stage 1 about the general system configuration, and the input-output training patterns, obtained from the training signal as an input-output table. In one embodiment, the teaching signal is obtained using structure presented in
In Stage 3 precedent part of the rule base is created and rules are ranked according to their firing strength. Rules with high firing strength are kept, whereas weak rules with small firing strength are eliminated.
In Stage 4, a second GA (GA2) optimizes a rule base, using the fuzzy model obtained in Stage 1, optimal linguistic variable parameters obtained in Stage 2, selected set of rules obtained in Stage 3 and the teaching signal.
In Stage 5, the structure of FNN is further optimized. In order to reach the optimal structure, the classical derivative-based optimization procedures can be used, with a combination of initial conditions for back propagation, obtained from previous optimization stages. The result of Stage 5 is a specification of fuzzy inference structure that is optimal for the plant 120. Stage 5 is optional and can be bypassed. If Stage 5 is bypassed, then the FIS structure obtained with the GAs of Stages 2 and 4 is used.
In one embodiment Stage 5 can be realized as a GA which further optimizes the structure of the linguistic variables, using set of rules obtained in the Stage 3 and 4. In this case only parameters of the membership functions is modified in order to reduce approximation error.
In one embodiment of Stage 4 and Stage 5, selected components of the KB are optimized. In one embodiment, if KB has more than one output signals, the consequent part of the rules may be optimized independently for each output in Stage 4. In one embodiment if KB has more than one input, membership functions of selected inputs are optimized in Stage 5.
In one embodiment, while Stage 4 and Stage 5 the actual plant response in form of the fitness function can be used as performance criteria of FIS structure while GA optimization.
In one embodiment, the SC optimizer 242 uses a GA approach to solve optimization problems related with choosing the number of membership functions, the types and parameters of the membership functions, optimization of fuzzy rules and refinement of KB.
GA optimizers are often computationally expensive because each chromosome created during genetic operations is evaluated according to a fitness function. For example a GA with a population size of 100 chromosomes evolved 100 generations, may require up to 10000 calculations of the fitness function. Usually this number is smaller, since it is possible to keep track of chromosomes and avoid re-evaluation. Nevertheless, the total number of calculations is typically much greater than the number of evaluations required by some sophisticated classical optimization algorithm. This computational complexity is a payback for the robustness obtained when a GA is used. The large number of evaluations acts as a practical constraint on applications using a GA. This practical constraint on the GA makes it worthwhile to develop simpler fitness functions by dividing the extraction of the KB of the FIS into several simpler tasks, such as: define the number and shape of membership functions; select optimal rules; fix optimal rules structure; and refine the KB structure. Each of these tasks is discussed in more detail below. In some sense SC optimizer 242 uses divide and conquer type of algorithm applied to the KB optimization problem.
Definition of the Numbers and of Shapes of the Membership Functions with GA
In one embodiment the teaching signal, representing one or more input signals and one or more output signals, can be presented as shown in the
Each component of the teaching signal (input or output) is assigned to a corresponding linguistic variable, in order to explain the signal characteristics using linguistic terms. Each linguistic variable is described by some unknown number of membership functions, like "Large", "Medium", "Small", etc.
"Vertical relations" represent the explicitness of the linguistic representation of the concrete signal, e.g. how the membership functions is related to the concrete linguistic variable. Increasing the number of vertical relations will increase the number of membership functions, and as a result will increase the correspondence between possible states of the original signal, and its linguistic representation. An infinite number of vertical relations would provide an exact correspondence between signal and its linguistic representation, because to each possible value of the signal would be assigned a membership function, but in this case the situations as "over learning" may occur. Smaller number of vertical relations will increase the robustness, since some small variations of the signal will not affect much the linguistic representation. The balance between robustness and precision is a very important moment in design of the intelligent systems, and usually this task is solved by Human expert.
"Horizontal relations" represent the relationships between different linguistic variables. Selected horizontal relations can be used to form components of the linguistic rules.
To define the "horizontal" and "vertical" relations mathematically, consider a teaching signal:
t=1, . . . , N—time stamps;
N—number of samples in the teaching signal;
x(t)=(x1(t), . . . , xm(t))—input components;
y(t)=(y1(t), . . . yn(t))—output components.
Define the linguistic variables for each of the components. A linguistic variable is usually defined as a quintuple: (x,T(x),U,G,M), where x is the name of the variable, T(x) is a term set of the x, that is the set of the names of the linguistic values of x, with a fuzzy set defined in U as a value, G is a syntax rule for the generation of the names of the values of the x and M is a semantic rule for the association of each value with its meaning. In the present case, x is associated with the signal name from x or y, term set T(x) is defined using vertical relations, U is a signal range. In some cases one can use normalized teaching signals, then the range of U is [0,1]. The syntax rule G in the linguistic variable optimization can be omitted, and replaced by indexing of the corresponding variables and their fuzzy sets.
Semantic rule M varies depending on the structure of the FIS, and on the choice of the fuzzy model. For the representation of all signals in the system, it is necessary to define m+n linguistic variables:
Let[X,Y], X=(X1, . . . , Xm), Y=(Y1, . . . , Yn) be the set of the linguistic variables associated with the input and output signals correspondingly. Then for each linguistic variable one can define a certain number of fuzzy sets to represent the variable:
Usually, at this stage of the definition of the KB, the parameters of the fuzzy sets are unknown, and it may be difficult to judge how many membership functions are necessary to describe a signal. In this case, the number of membership functions lX
Knowing the number of membership functions, it is possible to introduce a constraint on the possibility of activation of each fuzzy set, denoted as pX
One of the possible constraints can be introduced as:
This constraint will cluster the signal into the regions with equal probability, which is equal to division of the signal's histogram into curvilinear trapezoids of the same surface area. Supports of the fuzzy sets in this case are equal or greater to the base of the corresponding trapezoid. How much greater the support of the fuzzy set should be, can be defined from an overlap parameter. For example, the overlap parameter takes zero, when there is no overlap between two attached trapezoids. If it is greater than zero then there is some overlap. The areas with higher probability will have in this case "sharper" membership functions. Thus, the overlap parameter is another candidate for the GA1 search. The fuzzy sets obtained in this case will have uniform possibility of activation.
Modal values of the fuzzy sets can be selected as points of the highest possibility, if the membership function has unsymmetrical shape, and as a middle of the corresponding trapezoid base in the case of symmetric shape. Thus one can set the type of the membership functions for each signal as a third parameter for the GA1.
The relation between the possibility of the fuzzy set and its membership function shape can also be found. The possibility of activation of each membership function is calculated as follows:
Mutual possibility of activation of different membership functions can be defined as:
In fuzzy logic literature, T-norm, denoted as * is a two-place function from [0,1]�[0,1] to [0,1]. It represents a fuzzy intersection operation and can be interpreted as minimum operation, or algebraic product, or bounded product or drastic product. S-conorm, denoted by {dot over (+)}, is a two-place function, from [0,1]�[0,1] to [0,1]. It represents a fuzzy union operation and can be interpreted as algebraic sum, or bounded sum and drastic sum. Typical T-norm and S-conorm operators are presented in the Table 1.
If i=k, and j≠l, then equation (1.2) defines "vertical relations"; and if i≠k, then equation (1.2) defines "horizontal relations". The measure of the "vertical" and of the "horizontal" relations is a mutual possibility of the occurrence of the membership functions, connected to the correspondent relation.
The set of the linguistic variables is considered as optimal, when the total measure of "horizontal relations" is maximized, subject to the minimum of the "vertical relations".
Hence, one can define a fitness function for the GA1 which will optimize the number and shape of membership functions as a maximum of the quantity, defined by equation (1.2), with minimum of the quantity, defined by equation (1.1).
The chromosomes of the GA1 for optimization of linguistic variables according to Equations (1.1) and (1.2) have the following structure:
Another approach to the fitness function calculation is based on the Shannon information entropy. In this case instead of the equations (1.1) and (1.2), for the fitness function representation one can use the following information quantity taken from the analogy with information theory:
In this case, GA1 will maximize the quantity of mutual information (1.2a), subject to the minimum of the information about each signal (1.1a). In one embodiment the combination of information and probabilistic approach can also be used.
In case of the optimization of number and shapes of membership functions in Sugeno—type FIS, it is enough to include into GA chromosomes only the input linguistic variables. The detailed fitness functions for the different types of fuzzy models will be presented in the following sections, since it is more related with the optimization of the structure of the rules.
Results of the membership function optimization GA1 are shown in
Optimal Rules Selection
Rule Pre-selection Algorithm
The pre-selection algorithm selects the number of optimal rules and their premise structure prior optimization of the consequent part.
Consider the structure of the first fuzzy rule of the rule base
m is the number of inputs;
n is the number of outputs;
xi(t), i=1, . . . , m are input signals;
yj(t), j=1, . . . , n are output signals;
μk l
k=1, . . . , m+n are the indexes of linguistic variables;
lk=2, 3, . . . are the numbers of the membership functions of each linguistic variable;
μk {l
{lk} means the selection of one of the possible indexes; and
t is a time stamp.
Consider the antecedent part of the rule:
In general case, here can be used any of the T-norm operations.
The total firing strength Rθs 1 of the rule, the quantity Rƒs 1(t) can be calculated as follows:
In a similar manner the firing strength of each s-th rule is calculated as:
N—number of points in the teaching signal or maximum of t in continuous case.
In one embodiment the local firing strength of the rule can be calculated in this case instead of integration, the maximum operation is taken in Eq. (1.3):
In this case, the total strength of all rules will be:
Quantity Rƒs is important since it shows in a single value the integral characteristic of the rule base. This value can be used as a fitness function which optimizes the shape parameters of the membership functions of the input linguistic variables, and its maximum guaranties that antecedent part of the KB describes well the mutual behavior of the input signals. Note that this quantity coincides with the "horizontal relations," introduced in the previous section, thus it is optimized automatically by GA1.
Alternatively, if the structure of the input membership functions is already fixed, the quantities Rƒs s can be used for selection of the certain number of fuzzy rules. Many hardware implementations of FCs have limits that constrain, in one embodiment, the total possible number of rules. In this case, knowing the hardware limit L of a certain hardware implementation of the FC, the algorithm can select L≦L0 of rules according to a descending order of the quantities Rƒs s. Rules with zero firing strength can be omitted.
It is generally advantageous to calculate the history of membership functions activation prior to the calculation of the rule firing strength, since the same fuzzy sets are participating in different rules. In order to reduce the total computational complexity, the membership function calculation is called in the moment t only if its argument x(t) is within its support. For Gaussian-type membership functions, support can be taken as the square root of the variance value σ2.
An example of the rule pre-selection algorithm is shown in the
This algorithm produces an optimal configuration of the antecedent part of the rules prior to the optimization of the rules. Optimization of the consequential part of KB can be applied directly to the optimal rules only, without unnecessary calculations of the "un-optimal rules". This process can also be used to define a search space for the GA (GA2), which finds the output (consequential) part of the rule.
Optimal Selection of Consequental Part of KB with GA2
A chromosome for the GA2 which specifies the structure of the output part of the rules can be defined as:
Ii are groups of genes which code single rule;
Ik are indexes of the membership functions of the output variables;
n is the number of outputs; and
M is the number of rules.
In one embodiment the history of the activation of the rules can be associated with the history of the activations of membership functions of output variables or with some intervals of the output signal in the Sugeno fuzzy inference case. Thus, it is possible to define which output membership functions can possibly be activated by the certain rule. This allows reduction of the alphabet for the indexes of the output variable membership functions from
Thus the total search space of the GA is reduced. In cases where only one output membership function is activated by some rule, such a rule can be defined automatically, without GA2 optimization.
In one embodiment in case of Sugeno 0 order FIS, instead of indexes of output membership functions, corresponding intervals of the output signals can be taken as a search space.
For some combinations of the input-output pairs of the teaching signal, the same rules and the same membership functions are activated. Such combinations are uninteresting from the rule optimization view point, and hence can be removed from the teaching signal, reducing the number of input-output pairs, and as a result total number of calculations. The total number of points in the teaching signal (t) in this case will be equal to the number of rules plus the number of conflicting points (points when the same inputs result in different output values).
Fitness Evaluation in GA2
The previous section described optimization of the FIS, without the details into the type of FIS selection. In one embodiment, the fitness function used in the GA2 depends, at least in part, on the type of the optimized FIS. Examples of fitness functions for the Mamdani, Sugeno and/or Tsukamoto FIS models are described herein. One of ordinary skill in the art will recognize that other fuzzy models can be used as well.
Define error Ep as a difference between the output part of teaching signal and the FIS output as:
For the Mamdani model, the function F(x1 p, x2 p, . . . , xn p) is defined as:
Typical rules in the Sugeno fuzzy model can be expressed as follows:
The output of Sugeno FIS is calculated as follows:
Typical rules in the first-order Sugeno fuzzy model can be expressed as follows:
Typical rules in the zero-order Sugeno FIS can be expressed as follows:
The typical rule in the Tsukamoto FIS is:
where j1εIm
The output of the Tsukamoto FIS is calculated as follows:
Stage 4 described above generates a KB with required robustness and performance for many practical control system design applications. If performance of the KB generated in Stage 4 is, for some reasons, insufficient, then the KB refinement algorithm of Stage 5 can be applied.
In one embodiment, the Stage 5 refinement process of the KB structure is realized as another GA (GA3), with the search space from the parameters of the linguistic variables. In one embodiment the chromosome of GA3 can have the following structure:
In this case the quantities Δi are modifiers of the parameters of the corresponding fuzzy set, and the GA3 finds these modifiers according to the fitness function as a minimum of the fuzzy inference error. In such an embodiment, the refined KB has the parameters of the membership functions obtained from the original KB parameters by adding the modifiers prmnew i=prmi+Δi.
Different fuzzy membership function can have the same number of parameters, for example Gaussian membership functions have two parameters, as a modal value and variance. Iso-scalene triangular membership functions also have two parameters. In this case, it is advantageous to introduce classification of the membership functions regarding the number of parameters, and to introduce to GA3 the possibility to modify not only parameters of the membership functions, but also the type of the membership functions, form the same class. Classification of the fuzzy membership functions regarding the number of parameters is presented in the Table 2.
GA3 improves fuzzy inference quality in terms of the approximation error, but may cause over learning, making the KB too sensitive to the input. In one embodiment a fitness function for rule base optimization is used. In one embodiment, an information-based fitness function is used. In another embodiment the fitness function used for membership function optimization in GA1 is used. To reduce the search space, the refinement algorithm can be applied only to some selected parameters of the KB. In one embodiment refinement algorithm can be applied to selected linguistic variables only.
The structure realizing evaluation procedure of GA2 or GA3 is shown in
In one embodiment evaluation function calculation block 17104 calculates approximation error as a weighted sum of the outputs of the adder 17103.
In one embodiment evaluation function calculation block 17104 calculates the information entropy of the normalized approximation error.
Optimization of KB Based on Plant Response
In one embodiment of Stages 4 and 5 the fitness function of GA can be represented as some external function Fitness=ƒ(KB), which accepts as a parameter the KB and as output provides KB performance. In one embodiment, the function ƒ includes the model of an actual plant controlled by the system with FC. In this embodiment, the plant model in addition to plant dynamics provides for the evaluation function.
In one embodiment function ƒ might be an actual plant controlled by an adaptive P(I)D controller with coefficient gains scheduled by FC and measurement system provides as an output some performance index of the KB.
In one embodiment the output of the plant provides data for calculation of the entropy production rate of the plant and of the control system while the plant is controlled by the FC with the structure from the the KB.
In one embodiment, the evaluation function is not necessarily related to the mechanical characteristics of the motion of the plant (such as, for example, in one embodiment control error) but it may reflect requirements from the other viewpoints such as, for example, entropy produced by the system, or harshness and or bad feelings of the operator expressed in terms of the frequency characteristics of the plant dynamic motion and so on.
In one embodiment, the evaluation function calculation block 18107 forms evaluation function as a minimum of the entropy production rate of the plant 18103 and of the P(I)D controller 18102.
In one embodiment, the evaluation function calculation block 18107 applies Fast Fourier Transformation on one or more outputs of the measurement system 18105, to extract one or more frequency characteristics of the plant output for the evaluation.
In one embodiment, the KB evaluation system based on plant dynamics 18100 uses a nonlinear model of the plant 18103.
In one embodiment, the KB evaluation system based on plant dynamics 18100 is realized as an actual plant with one or more parameters controlled by the adaptive P(I)D controller 18102 with control gains scheduled by the FC 18101.
In one embodiment plant 18103 is a stable plant.
In one embodiment plant 18103 is an unstable plant.
The output of the SC optimizer 18001 is an optimal KB 18002.
Teaching Signal Acquisition
In the previous sections it was stated that the SC optimizer 242 uses as an input the teaching signal which contains the plant response for the optimal control signal.
The structure presented in
The control signal evaluation system based on plant dynamics 19100 includes the buffer 19101, the adaptive P(I)D controller 19102 which uses Buffer 19101 as a scheduler of the coefficient gains, the plant 19103, the stochastic excitation generation system 19104, the measurement system 19105, the adder 19106, and the evaluation function calculation block 19107. Output of the P(I)D controller 19102 is provided as a control force to the plant 19103 and as a first input to the evaluation function calculation block 19107. Output of the excitation generation system 19104 is provided to the Plant 19103 to simulate an operational environment. An output of Plant 19103 is provided to the measurement system 19105. An output of the measurement system 19105 is provided to the negative input of the adder 19106 and together with the reference input Xref forms in adder 19106 control error which is provided as an input to P(I)D controller 19102. An output of the measurement system 19105 is provided as a second input of the evaluation function calculation block 19107. The evaluation function calculation block 19107 forms the evaluation function of the control signal and provides it to the fitness function input of the SSCQ 19001. The fitness function block of the SSCQ 19001 ranks the evaluation value of the control signal presented in the current chromosome into the fitness scale according to the current parameters of the GA0.
An output of the SSCQ 19001 is the optimal control signal 19002.
In one embodiment, the teaching for the SC optimizer 242 is obtained from the optimal control signal 19002 as shown in
In one embodiment optimal plant response 20003 can be transformed in a manner that provides better performance of the final FIS.
In one embodiment high and/or low and/or band pass filter is applied to the measured optimal plant response 20003 prior to optimal teaching signal 20004 formation.
In one embodiment detrending and/or differentiation and/or integration operation is applied to the measured optimal plant response 20003 prior to optimal teaching signal 20004 formation.
In one embodiment other operations which the person skill of art may provide is applied to the measured optimal plant response 20003 prior to optimal teaching signal 20004 formation.
Simulation Results
The dynamic system used for the results in
u(t)=kpe+kdė+ki∫0e(t)dt (e=yref−y) is a controlling force; and
ξ(t) is a stochastic excitation.
The entropy production rate of the dynamic system is:
The kinetic energy is:
The potential energy is:
The total energy is: T+U.
The control system's entropy production rate is:
Model parameters used for simulation are:
Initial conditions are taken as:
Stochastic excitation used for the simulations is Raleigh noise, obtained using a stochastic filter. A time history and the histogram of such a noise is shown in
The GA parameters used in the SSCQ 130 for the simulation results are: PS: 200; GN: 100; Pcr=0.9; Pmut=0.006; and two point crossover was used. The results of the stochastic simulations under different types of control are presented in the
SC optimizer 242 in the simulation results corresponding to FIGS. 21–C17 uses the Mamdani type fuzzy model with 4 membership functions for the first input, 4 membership functions for the second input, 5 membership functions for the third input, 3 membership functions for the first and second outputs, and 6 membership functions for the third output. The number of membership functions as, well as their types, were obtained by genetic optimization. For the AFM, the number of membership functions for the inputs was specified manually (7 membership functions per each input). The numbers of the membership functions for outputs is simply equal to the number of rules, e.g. 51 membership functions for each output.
Swing dynamic system simulation results, Motion under fuzzy control with one P(I)D Controller. Comparison between back propagation FNN and SC optimizer control results
The previous example showed simulated control of a stable plant. The SC optimizer 242 can also be used to optimize a KB for an unstable object as, for in one embodiment, a nonlinear swing dynamic system. The nonlinear equations of motion of the swing dynamic system are:
Here ξ(t) is the given stochastic excitation (a white noise). Equations of entropy production are the following:
The system (38) is a globally unstable system (in Lyapunov sense).
In this example only the second state variable (the length l) is controlled, and behavior of the first state variable (the rotation angle θ) is considered only for the reference.
The fitness function for the unstable swing is configured to minimize the entropy production rate in the plant and to minimize the entropy production rate in the control system. The final form of the fitness function of control in this case is:
For the simulation, initial conditions and system parameters were specified as shown in Table 3 below.
Both the FNN controller and the SC optimizer-based controller are better than the P(I)D controller. The FNN approximates the teaching signal with redundant accuracy, and, as a result, better performance with the same conditions as used for teaching signal acquisition, but control signals are unstable near equilibrium points. The SC optimizer control has better performance with respect to entropy production, and control gains have simpler physical realization. The output of the SC optimizer-based controller is stable near equilibrium points. The KB prepared with the SC optimizer uses 24 rules, and has almost the same performance (according to the selected fitness function) as the FNN based FC with 64 rules
For analysis of the robustness of the simulated FC, the simulations were repeated with a new excitation signal, having longer duration, and different trajectory using the same distribution as was used for the teaching signal acquisition. The excitation used in this case is shown in
The results of the intended fitness function are shown in
Table 6 shows that both the FNN controller and the SC optimizer-based controller are better than the P(I)D controller regarding fitness function performance. Due to over learning, the FNN controller becomes unstable with unknown excitation, and asymptotically looses control under the intended fitness function. The SC optimizer control works better under unknown conditions, thus the FC prepared with a KB produced by the SC optimizer 242 is more robust regarding variations of the excitation signal from the same distribution.
In the following example in addition to unknown excitation, a new reference signal in introduced as a harmonic signal obtained by the following equation:
The reference signal according to Equation (41) is shown in the
Table 7 shows that the FC prepared with a KB generated by the SC optimizer 242 is more robust in the presence of reference signal variation. Thus, the SC optimizer creates a robust KB for FC and reduces the number of rules in comparison with a KB created with other approaches. The KB created by the SC optimizer 242 automatically has a relatively more optimal number of rules based. The KB created by the SC optimizer 242 tends to be smaller and thus more computationally efficient. The KB created by the SC optimizer tends to be more robust for excitation signal variation as well as for reference signal variation.
Swing dynamic system simulation results, Motion under fuzzy control with two P(I)D Controllers. Comparison between back propagation FNN and SC optimizer control results
In one embodiment two state variables (the angle θ and the length l) are controlled, and two types of stochastic excitations are used.
Gaussian excitation (a white noise) is acting along θ-axis, and non-Gaussian (Rayleigh) excitation is acting along l-axis. Initial conditions: θ0=0.25, l0=2.5, {dot over (θ)}0=0, {dot over (l)}0=0.01, and reference signals: θ=0.4; l=3.5. In this example we see Sugeno 0 FIS with four inputs and six outputs variables. Input variables are: control error, derivative of control error for two P(I)D Controllers (along θ and l-axes). Output variables are control gains for P(I)D θ and P(I)D l correspondingly. For fuzzy simulation in this case we have chosen fitness function which minimizes a control error.
Tables 43, 44 and
Table 8 shows dynamic and thermodynamic characteristics of swing motion along θ-axis.
Table 9 shows dynamic and thermodynamic characteristics of swing motion along l-axis.
In Tables 10 and 11 and in
In this case the output of FC_FNN gives unacceptable control of the swing motion.
In Table 11, the FC_FNN gives unacceptable control of the swing motion under unknown conditions. The simulation results show that the FC with the KB generated by the SC optimizer is more effective and robust than P(I)D and FNN control under new conditions such as different excitations, different reference signal and different initial conditions.
Although the foregoing has been a description and illustration of specific embodiments of the invention, various modifications and changes can be made thereto by persons skilled in the art, without departing from the scope and spirit of the invention as defined by the claims attached hereto.
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Not a chance of rain, is there? Looks a little dark
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Not a chance of rain, is there? Looks a little dark
It's been looking dark in Dayton most of the day, but rain isn't predicted, at least for the next few hours. Just warm and humid.
thank goodness that quit carrying
time to see if anyone gets plunked..
doubt it
Discovered something new and I love it. I bought the kids a xbox 360 a few weeks back...I'm able to watch mlb.tv on this baby. Since that's the only way I can watch the Reds (mlb.tv) I've been anchored at my computer desk to watch the Reds on my little 20 inch monitor. Well what do you know, this works perfectly. :O)
Choo with a ground-rule bouncer to start things off.
lol, Choo needs some glasses thinking that was a HR
great bunt from Cozart!
Choo wanted to see if the umpires were paying attention.
Yeah, sweet bunt there to make it first and third.
well, if they were planning on plunking Votto or BP, not gonna do it now
Nice bunt hit there by Cozart


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Gold'n Sands
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Location: Sun Over Beach, Oz
Oddometer: 3,882
In the twenty minutes it takes to reach Keith township, the tyre deflates again and I'm rolling on a dead flat tyre as I reach the park where an unveiling ceremony for the memorial to Dakar rider and local, Andy Caldecott, was held at 11.30am. It's a bit past 1pm, so all those delays really conspired against me. I'm really disappointed that I've missed this event. It was the best I could do to get here though, short of simply riding down the mind-numbing Western Highway. I catch up briefly with fellow Adelaide ADVrider, Kevs69, and his lovely partner before they disappear back west on a tidy BMW R1150GS. Another ADVrider riding mate, GoneAgain, disappeared back homewards only five minutes before I arrived. But there's some friendly locals and guests who've come to the event to honour Andy and family still enjoying a barbeque and beers in the park. After dragging the bike up onto its centre stand and chatting for ages with a local rider, I grab a sausage sandwich and wander over to view the freshly unveiled monument. It's a beauty. A truly fine way for Andy to be remembered by locals and travellers alike.
After this, I spend the next couple of hours chatting with dozens of locals and fellow riders as I peel the rear tyre off the rim and patch the tube. What should be a short job is made quite lengthy - it's never a quick job when you've got a hundred bike riders all keen to say G'day and chat about the old Tenere and their own adventures.
By about 4.30pm, I'm mobile again after refueling. I wish I could head to the pub for a beer with some of Andy's mates, but I need to keep moving east. It'd be very easy to end the day in the pub, but it was never my plan to remain in Keith after the memorial service.
Sorry, Andy, but I know you'd understand.
Tree hugger.
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Release Date: 0000-00-00
Genre: Musical, Drama
Rating: NR
Drona is the only son of Mukhesh Rushi and Sita. Drona's father Mukesh Rushi is an honest and dedicated Police officer. Drona likes his mother Meenakshi so much and loves her as he loves his own life. When he was a child Drona stole his father's revolver to scare his friends. When his father found out, he scolded him. Consequently Drona left his home and got lost. After 10 years, he comes back to his parents with the name Chandu. A neighboring girl Indu likes Drona/Chandu a lot. On a fateful day, she finds out that Chandu is none other than the long lost boy, Drona.
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SHUT UP N' PLAY IS BACK with all new designs and the same no-nonsense attitude. Let the world know that you take your sports seriously. Choose your sport and make a stand today.The Shut Up N' Play apparel line came to prominence back in the mid-nineties when just about every professional player of note could be seen wearing one or more of the SUNP line.The message of Shut Up N' Play was an instant hit all over the country for those who let their performance speak for itself and left the whining and lip service in the dugout. The SUNP message has never been more appreciated in today's sports environment. Once again, players and fans alike can stand up and simply state, "Shut Up N' Play!"
Latest Posts:
Chipper Jones on the cover of Beckett magazine
Chipper is sporting his Shut Up N Play mock on his Pinnacle 96' Baseball Cards. Christie Brinkley (Yes that Super Model) was behind the cameras on this photo shoot with the NL MVP and 8 time all-star third baseman with the Atlanta Braves. Hello Cooperstown when he finally retires. Aug. 09, 2012 | Comments: none [...]
Bernie Williams 4 time Gold Glove Centerfielder of the NY Yankees
Bernie is accepting his AL Championship MVP Award in 1996. As you can see Bernie is wearing his Shut Up N Play game worn T-shirt drenched in victory champagne. Shut Up N Play was Bernie's battle cry along with most of the World Series Champions.
Shut Up N' Play interview 1995
Tim Alders, owner and president of Shut Up N' Play, appeared on the MSG sports broadcasting network. The interview took place on the infield at the Ballpark in Arlington, home of the Texas Rangers. Mr. Alders discusses the origins of SUNP and the line as it existed at that time in 1995.


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A night of food, wine and poker Oxnard will benefit five charities
Five nonprofits will benefit from a Progressive Poker Food & Wine Event presented by Community Among Friends on Saturday in downtown Oxnard.
The event will feature food and wine tasting at five restaurants within walking distance: The Kitchen, 5th Street Steak and Seafood, Sugar Beets Restaurant & Bar, Fresh & Fabulous Café and Casa Lopez.
At each site, guests will get a playing card that will ultimately make a poker hand. At the last stop, players receive their last card and play their hand, with the winner receiving $500 in cash and four Dodgers dugout club tickets against the Giants with parking, a prize valued at $2,000.
Event organizers hope to raise $40,000. Proceeds will help the Salvation Army's dental clinic buy a digital X-ray machine, said Judy Cofer, of Camarillo, chairwoman of the advisory board.
"This event is a great opportunity for us to reach out into the community and let people know what we can do to help them in their everyday lives," Cofer said.
Gull Wings Children's Museum will add the funds to its exhibit development and building expansion fund, said Executive Director Julie Chambers, of Moorpark.
Proceeds will help move Gull Wings to a new Ventura County location with double or triple the room for visitors to explore exhibits and programs for children age 1 to 12.
"An event like this ... allows Gull Wings to raise awareness of our goals, programs and to some new friends knowledge of our very existence," Chambers said.
Big Brothers Big Sisters will use the funds to support youth mentoring in public schools in Oxnard, said Pedro A. Chavez, of Camarillo, a volunteer for three years.
"Children with a mentor show measurable differences in their personal and academic lives," Chavez said. "They are more confident in their schoolwork performance, they get along better with their families, and they are more likely to stay in school."
Proceeds will enable the Children's Wall of Tears to save more children's lives through education, said Jane LeMond-Alvarez, of Oxnard, who founded the nonprofit with her husband, Leo Alvarez.
"The funding we receive will ensure that The Children's Wall of Tears can travel across the countryside this summer to raise the awareness of child cruelty in our nation," LeMond-Alvarez said.
Event sponsors include the Republic Services of Oxnard, Oxnard Pallet Co., JT's Rubbish and Recycle LLC, Prime Time Surface Cleaning, SA Recycling, VC Buzz, Totally Local VC, Labor Ready, Ventura County Credit Union, Service Master Building Maintenance, Standard Industries and Oxnard Downtown Management District Office.
Tickets are available through the five participating charities: Big Brothers Big Sisters of Ventura County, 484-2282, ext. 16; The Salvation Army, 985-7595; FOOD Share, 983-7100, ext. 129; the National Association Against Child Cruelty, 320-7625; and Gull Wings Children's Museum, 483-3005.
The cost is $40 in advance or $50 at the door. For more information, visit http://communityamong friends.org/poker-food-wine-Oxnard.
If you go
What: Second Progressive Poker Food & Wine Event
When: June 1; 6 to 9:30 p.m. with happy hour from 5 to 6 p.m.
Where: Five restaurants within walking distance in downtown Oxnard
Cost: $40 in advance; $50 at the door
Information: http://communitamongfriends.org/poker-food-wine-oxnard/
Subscribe to the Ventura County Star
Need Help? Call us at 1-877-710-6182.
Monday-Friday: 6am-3pm / Saturday: 6:30am-10:30am / Sunday: 7am-11am


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Routine tests for
type 1 diabetes include an A1c or similar
test (glycosylated hemoglobin or
glycohemoglobin) that estimates your average blood
sugar level over the previous 2 to 3 months. It helps monitor blood sugar
control after treatment has started.
You need to see your doctor
about every 3 to 6 months throughout your life for exams and tests to watch
your condition and adjust your treatment.
For more information,
see the
schedule for exams and tests beginning at diagnosis.
After you have
had diabetes for 3 to 5 years, you will need
annual tests to watch for signs of damage to your eyes
(diabetic retinopathy), kidneys (diabetic nephropathy), heart, blood vessels,
and nerves (diabetic neuropathy). If your child has diabetes, this testing
should begin at
Other tests
You may need a
thyroid-stimulating hormone (TSH) test when type 1
diabetes is diagnosed and then every 1 to 2 years. This test checks for thyroid
problems, which are common among people with diabetes.
You may have found out that
you have type 1 diabetes when your insulin levels dropped very low and you were
admitted to a hospital for
diabetic ketoacidosis (DKA). The following tests were
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I'm surprised that you can even spell Kucinich.
You understand about as much economics as a high school graduate from a Rockford High School. Of course economics 101 isn't taught in Rockford, if it was (205) wouldn't be in the finical problem they are in.
- Remember, fireworks are dangerous
- Wallace asks citizens to fight cuts
- Dispute over state payroll rolls on
- Why fight over free trade confounds partisan divide
- Still no state budget
- Crime control is not the responsibility of landlords
- Fly over to the Poplar Grove Wings and Wheels Museum benefit
- Local leaders warn of budget deadlock's impact
- SHUTDOWN: Illinois preps for the worst
- TRRT Online Edition | July 1-7
Kucinich has a bill to address the budget
Feb. 13, Speaker John Boehner addressed a letter to President Obama, "[T]o support the creation of private-sector jobs, immediate action is needed to rein in federal spending."
He's wrong.
According to the Economic Policy Institute, John Boehner's budget would reduce federal deficits by 4 percent while costing 700,000 American jobs. Boehner's response was, "So be it."
Both Boehner's and Obama's deficit-trimming budgets operate under the same flawed assumption: ultimately the government's budget must be balanced.
If the federal government originates money, why not print the money to pay the national debt? All non-coin money is loaned into existence by the privately-owned Federal Reserve and banks. Debt-based money cannot pay off the debt.
Fortunately, a member of Congress is taking action. Representative Dennis Kucinich (D-OH) introduced the National Employment Emergency Defense (NEED) Act last December. The bill will:
1. Nationalize the Federal Reserve.
2. Eliminate the banks' privilege to create money.
3. Spend new money into circulation on a national infrastructure program.
Our inflationary credit money will be replaced by a sound currency. The federal debt will be paid as it comes due. It is change we can believe in. We must support Dennis to make NEED a reality.
Jules Brouillet
Palatine, Ill.
From the March 16-22, 2011, issue
One Comment


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NWSB Mariners blogger Brian Scott weighs in on the MLB trade rumors and gossip surrounding the 2014 Seattle Mariners season. Some possible names? Infielders of course. Neither Brad Miller or Nick Franklin have been able to seize the opportunity and take ownership of the spot. Both have batted well below .200 on the season and that's just not going to work long term.
Trade Talk: Are The Seattle Mariners Buyers Or Sellers In 2014?
One Third Of The Seattle Mariners Season Is In The Books
With Maurer Struggling, M's Need Walker & Paxton Back
Your 2014 Seattle Mariners, Are A +.500 Team
Welcome Back, Hisashi Iwakuma
Seattle Mariners 2014, 3 Week Pitching Report
The Hits Just Keep On Coming The news on the pitching front continues to worsen for the Seattle Mariners who seem lost at sea in the midst of an eight game losing streak. Injuries, youth, defensive miscues, and flat out incompetence have all contributed to the mess the M's hurlers find themselves currently in. Let's […]
The Seattle Mariners Run Aground For 8th Straight Loss
Seattle Mariners starting pitchers off to blazing start
Ladies & Gents!!! Your 2014 Seattle Mariners!!!!!
Jack Zduriencik and the Mariners brass suddenly get cheap
Seattle Mariners post by Brian Scott. Randy Wolf pitched well enough to make this 2014 Mariners. In my opinion. It's definitely not happening now as Wolf was granted his release. Following his release, Wolf stated to the media that after the M's informed him he had made the team AND they wanted him to sign a 45 day advance release consent form. Bogus move.
Decision Time: Projecting the Seattle Mariner's 25 Man Roster for 2014
Tough calls for McClendon coming in just over a week. I think it's fair to say that there has been more intrigue in this year's Seattle Mariner's Spring Training Camp than any other for some time. With 2 starting positions up for grabs and a plethora of rotation spots to fill, some decisions will be […]
Seattle Mariners in need of another arm
NWSB Insider and Seattle Mariners Blogger Clinton Bell is back with an in depth piece about the top two free agent candidates the Mariners are looking at in terms of starting pitching. Ervin Santana or Chris Capuano? Capuano is the latest player to drop his name into the hat of Jack Z, but he may not be the best candidate.


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Gear Review
Blue Solex Mirror
Awesome lens. It's totally mirrored, but you can see through it but you gotta look pretty hard. These are way brighter than I thought which is perfect cuz already got the smokes. Might want a lens that blacks out more for pure bluebird days or if these are your only lens.
No worries though cuz it'll still get the job done
View More Details on Anon Hawkeye Replacement Goggle Lens


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Photo by An Sang
YIELD: Makes 64 mini-doughnuts
Heat oven to 350°F. Coat 2 cookie sheets with cooking spray. Cut dough into 4 equal pieces; cut each into 16 equal pieces. Roll each piece between fingers to form a 1 1/2-to 2-inch-long log. Pinch together ends of each log to form rings and place doughnuts 1 inch apart on cookie sheets; bake 1 batch at a time until dough puffs, 5 to 6 minutes. Whisk sugar and milk in a bowl until smooth. Dip top of each warm cookie into sugar-milk glaze; press into crushed candy. Let set on wire racks.
Per serving (1 cookie): 28 calories, 1 g fat, 0 g saturated, 5 g carbohydrates, 0 g fiber, 0 g protein
Nutritional analysis provided by Self


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The Hotel Indigo in downtown Asheville.
- Filed Under
The city has leased nearly half of its parking spaces in the AT&T lot on the corner of O. Henry Avenue and Haywood Street to Hotel Indigo.
Parking Services Manager Harry Brown said the lot, which the city leased from the communications company 18 months ago, has under-performed with only 30 percent of the 45 spaces full on a good day.
"On certain days, when you had a huge event at the civic center, you would get a lot of traffic there," he said. ...


text: Around 4,000 boxes of paper records fill the shelves in Central New Mexico Community College's storage area. And many of these boxes—those containing employee personnel files, for example—must remain in storage for up to 55 years before they can be destroyed. It's a big reason why CNMCC has embraced electronic records management.


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ENC Regional News
12:32 pm
Thu February 28, 2013
5,000 Gallons Of Sewage Enters Creek in Jacksonville
5,000 gallons of wastewater has spilled into a creek in Jacksonville, following more than an inch and a half of rain on Tuesday. The City's Maintenance and Utilities Superintendent Pete Deaver was at the site Thursday afternoon testing the water.
"There is nothing else we can do. The rain water helps it dilute the affluent that reaches the waters. We have made some repairs to the man hole where the overflow occurred."
The wastewater flowed into Scales Creek, located at the intersection of Ellis Boulevard and Lejeune Blv. Deaver says they will continue to monitor the water quality until it returns to normal.


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Winifred Haun & Dancers Hosts Fall 2012 Benefit in Hyde Park, 11/2
Winifred Haun & Dancers' Fall 2012 Benefit will take place in four days on Friday, November 2, from 7 to 9 p.m. at the home of Young-Kee Kim and Sydney Nagel, 4913 S. Kimbark, in Chicago's Hyde Park neighborhood. Street is parking available. Seating is limited.
Tickets: $99 general admission / $129 patron admissioin / $159 dance lover / $300 party of 4. Purchase tickets here. Only 13 seats left!
This very special event will support the creation of Ms. Haun's newest dance, which is based on how we move and feel in the different rooms in our houses. The evening will feature a preview of the new work, and other works, wine, an appetizer dinner and great conversation.
The evening will begin on the first floor with wine and appetizers. Then, we'll view two dances, one in the living room and one in the entranceway. Next, we will walk up to the second floor and see two more dances, a solo and a group section from the new work. Then, we'll walk up to the 3rd floor ballroom and see Bento and another section from the new work. The evening will end back on the 1st floor with dessert and a chance to talk about what you saw.
Winifred Haun & Dancers is supported, in part, by Black Canyon Cultural Arts Center, Community Bank of Oak Park & River Forest, Cheney Mansion, Pleasant Home, Legere Dance Center, Academy of Movement Music, Richard H. Driehaus Foundation, MetLife Foundation, Oak Park Area Arts Council, Illinois Arts Council, a state agency, Lou Conte Dance Studio at the Hubbard St. Dance Center, Ruth Page Center for the Arts, Voice of the City, a multi-arts organization and over 80 generous individuals.
You can find more info about Winifred Haun & Dancers on their website or Facebook.
Photo Credit: Matthew Gregory Hollis
|Related Links
|Winifred Haun & Dancers to Host Open Rehearsal as Part of Chicago Dance Month
April 10, 2015
|Winifred Haun & Dancers to Remount PROMISE in 2015
November 11, 2014
|Winifred Haun & Dancers Set for BREAKING ICE at Link's Hall, Now thru 10/18
October 16, 2014
|Winifred Haun & Dancers Set for BREAKING ICE at Link's Hall, 10/16-18
September 16, 2014
|Winifred Haun & Dancers to Kick Off Fall 2014 Open Rehearsals, 10/5
August 27, 2014
About Author
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- James Franco to Star in New Fox Comedy, WHY HIM?
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Samsung 840 Pro SSD review -
SSD Performance AS SSD Benchmark
AS SSD Benchmark
Added to the benchmark suite is this nice little German application called AS SSD Benchmark. This test gives an extensive result set. The test is popular, so just for reference we always show the results, here we go.
What continously jumps out is the fantastic write performance. Roughly 490 MB/sec sequential writes is downright spectacular.
AS-SSD returns a weighed score, I gathered some of the latest SSDs and placed them in a chart, this shows how fast the SSD really is.
Compression benchmark
Above Samsung 840 Pro 256GB SSD
This test creates test patterns on the target drive which are random and vary in the level of compression used in the test data. This ranges from 0% compressible to 100% compressible.
Basically for this chart you want a straight line as much as possible here (no compression performance loss) based upon the highest performance in MB/sec. As you can see, this is leading performance.
Above OCZ Vector 256GB SSD
Above Plextor M5 Pro 256GB SSD
Abobe Intel Series 335 240GB
Above Intel 520 series 240GB
Above OCZ Octane 512GB
Above Corsair Force 3
Above Corsair Force 100
In this review we test the new M.2. and mSATA models of the Samsung 850 EVO Series SSDs. Seriously nice storage technology as it is has hyper fast, yet is made to be very competitive in pricing. Arm...
Samsung T1 Portable SSD review
We review the Samsung T1 Portable SSD, the new portable storage unit allows you to connect it towards USB 3.0, combined with the DNA of an SSD it makes that a VERY fast storage unit. In fact this pr...
Samsung 850 EVO SSD review
In this review we test the 500GB Samsung 850 EVO SSD. The new SSD is a stunning series of storage technology As it is enthusiast class fast yet made to be very competitive in pricing. Armed with truck...
Samsung 850 Pro SSD review
In this review we test the 1TB Samsung 850 Pro SSD. A lovely piece of technology. An SSD that is extremely fast and actually amongst the handful of fastest storage units we have ever tested. Head on o...


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I've pretty much finished tweaking my personal blog [now back on Tumblr since the painful downtime seems to have disappeared]. This is a wider layout adapted from the single column one I had been using for a couple of months previously.
keyboard shortcuts: ← previous shot → next shot L or F like Z toggle zoom


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Nike Women's Gym Seamless Capris
$44.99 $60.00*
Save $15.01 (25%)
Product Information:
Attain ultimate comfort in the Nike® Women's Gym Seamless Capris. No matter what your training activities entail, these luxurious athletic bottoms will keep you comfortable and irritation-free. Moisture-wicking Dri-FIT® material will pull perspiration away from your body, leaving you dry and comfortable. Other key features include flat seams, a stretch waistband and breathable perforations for ventilation where you need it most.
- All-purpose seamless training capris
- Dri-FIT recycled polyester fabric to wick sweat away and help keep you dry and comfortable
- Strategically placed perforations for enhanced ventilation
- Stretch waist for a snug comfortable fit
- Flat-seams construction to minimize irritation caused by chafing
- Machine wash
- Fabric: Dri-FIT 94% nylon/6% spandex
- Style: 548671
- Nike
Country of Origin: VietnamWeb ID: 18665216
Customer Reviews:
Rated 4.7 out of 5�by 19 reviewers.
Rated 5 out of 5�by WIrunner Good buy Very comfortable and useful for many activities. Wish they had a key pocket. October 30, 2013
Rated 5 out of 5�by LG Love them!! I absolutely love these pants! I run anything from 5k's to half marathons and these are my new favorite pants. They fit wonderfully and don't move or bunch at all. I am a curvy person so I frequently have issues with clothes not staying where they are originally. I have recommended them to friends. June 18, 2013
Rated 5 out of 5�by Kelli My absolute favorite exercise pant. Love these for general fitness/running and yoga (esp. hot yoga). Long enough that they offer grip for certain yoga poses, without being too hot. Used them for a 5K in rainy weather and they remained reasonably dry. They offer quite a bit of give, without being "stretchy" like spandex. Just a great, all around training pant. I love the detail on the calf, as well. The only downside is the cost, which is to be expected. June 12, 2013
Rated 5 out of 5�by Kayla Love these! I absolutely LOVE these capris!! Hopefully They come back in stock in my size so I can buy another pair (or two)! October 23, 2014
Rated 5 out of 5�by Tammy Great fit Love the fabric great for running. Great value . Washes and dried very well . February 11, 2014
Rated 5 out of 5�by JennJenn Amazing Love .... Love .... Love these capris!!!! I really didn't want to pay the price for these but sooooo glad I did!!!! They fit great with just the right compression! I use mine for post race! They are stylish and fit amazing!!!!! Two thumbs up!!! January 19, 2014
Rated 3 out of 5�by tar Not exactly for the curvy gal I like these they're comfortable and way cute. If I weighed like 115 lbs I would definitely wear them a lot more. ...in public. ;) great for exercising in n I love how they feel. Great material. January 15, 2014
Rated 5 out of 5�by runner652 This product is very comfortable They are very comfortable. I would totally recommend someone to buy this product, especially if you want to workout or run. They're even great to just relax in. November 6, 2013
Rated 5 out of 5�by Lyric Love them!! I run and every half I have ran has been in these capris. I have many colors. October 29, 2013
Rated 5 out of 5�by Sosa007 My news favorite workout bottom!! Great product! Love the capris, my new favorite. The material is stretchy and hugs you in the right places. A must!! September 10, 2013


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Do you think Datto will turn this into a clan? If that's even possible?
"We're definitely not casuals."
What makes this group special? No casuals. By definition, if you are in this clan, you are so not a casual. Find new raid friends here, group up and take on the world. Open to all! Fans of Datto Does Destiny get 5% off. 5% off what? I dunno, I'm just trying to make you feel special.
Click tag to search for similarly tagged clans


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