1. A self-organizing sensor (SOS) for estimating a process variable of interest in a physical process, comprising: a) a steady-state model, including: i) a plurality of inputs, S 1 (t), S 2 (t),. .. , S L (t), wherein L is the number of input variables or parameters, and ii) an output Y S (t), which is a function of the inputs S 1 (t), S 2 (t),. .. , S L (t) in the following form: 
 Y S ( t )= F S [S 1 ( t ), S 2 ( t ),. .. , S L ( t )], wherein F S [.] is a function that describes the input and output relationship of the steady-state model; b) a dynamic model, including: i) a plurality of inputs, x 1 (t), x 2 (t),. .. , x M (t), wherein M is the number of input variables, and ii) an output Y D (t) that tracks the given trajectory of its target variable Y T (t) under process dynamic and operating condition changes; c) a converter that converts the output Y S (t) of the steady-state model to the target variable Y T (t); d) an adder that implements the following function: 
 e M ( t )= Y T ( t )− Y D ( t ); e) an objective function for the dynamic model that is in the following form: E M ⁡ ( t ) = ⁢ 1 2 ⁢ e M ⁡ ( t ) 2 = ⁢ 1 2 ⁡ [ Y T ⁡ ( t ) - Y D ⁡ ( t ) ] 2 ; and f) a combiner and switch mechanism that combines or selects the signals Y D (t) and Y T (t) to produce the final Soft-Sensor output Y(t); wherein the inputs of the steady-state model are in one or more of the following forms: i) process variables measured online from the process directly, ii) process variables or other information from the sensor networks, iii) internal variables or parameters of the SOS, and iv) parameters set from a computer or device.