# System Identification Toolbox

Create linear and nonlinear dynamic system models from measured input-output data

System Identification Toolbox™ provides MATLAB® functions, Simulink® blocks, and an app for constructing mathematical models of dynamic systems from measured input-output data. It lets you create and use models of dynamic systems not easily modeled from first principles or specifications. You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process models, and state-space models. The toolbox also provides algorithms for embedded online parameter estimation.

The toolbox provides identification techniques such as maximum likelihood, prediction-error minimization (PEM), and subspace system identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting.

### Get Started

Learn the basics of System Identification Toolbox

### Data Preparation

Plot, analyze, detrend, and filter time- and frequency-domain data, generate and import data

### Linear Model Identification

Identify impulse-response, frequency-response and parametric models, such as state-space and transfer function models

### Nonlinear Model Identification

Identify nonlinear ARX, Hammerstein-Wiener, and grey-box models

### Grey-Box Model Estimation

Estimate coefficients of linear and nonlinear differential, difference and state-space equations

### Model Validation

Compare model to measured output, residual analysis, response plots with confidence bounds

### Model Analysis

Discretize models, convert models to other types, linearize nonlinear models, simulate and predict output

### Time Series Analysis

Analyze time series data by identifying linear and nonlinear models, including AR, ARMA, and state-space models; forecast values

### Online Estimation

Estimate model parameters and states during system operation, generate code and deploy to embedded targets

##### Support Get trial now
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