Recursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filtering

2015 ◽  
Vol 84 (2) ◽  
pp. 1045-1053 ◽  
Author(s):  
Yanjiao Wang ◽  
Feng Ding
Keyword(s):  
Least Squares ◽  
Gradient Algorithm ◽  
Recursive Least Squares ◽  
Data Filtering ◽  
Recursive Least Squares Algorithm ◽  
Wiener Systems ◽  
Least Squares Algorithm

scholarly journals Filtering Based Recursive Least Squares Algorithm for Multi-Input Multioutput Hammerstein Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Ziyun Wang ◽  
Yan Wang ◽  
Zhicheng Ji
Keyword(s):  
Least Squares ◽  
Finite Impulse Response ◽  
Recursive Least Squares ◽  
Separation Principle ◽  
Data Filtering ◽  
Parameter Estimation Problem ◽  
Numerical Examples ◽  
Recursive Least Squares Algorithm ◽  
Noise Transfer Function ◽  
Least Squares Algorithm

This paper considers the parameter estimation problem for Hammerstein multi-input multioutput finite impulse response (FIR-MA) systems. Filtered by the noise transfer function, the FIR-MA model is transformed into a controlled autoregressive model. The key-term variable separation principle is used to derive a data filtering based recursive least squares algorithm. The numerical examples confirm that the proposed algorithm can estimate parameters more accurately and has a higher computational efficiency compared with the recursive least squares algorithm.

Parameter Estimation for Wiener Systems with Preload Nonlinearities

2014 ◽  
Vol 989-994 ◽  
pp. 1460-1463
Author(s):  
Yun Xia Ni ◽  
Jian Dong Cao
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Recursive Least Squares ◽  
Switching Function ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Recursive Least Squares Algorithm ◽  
Wiener Systems ◽  
Least Squares Algorithm ◽  
Identification Model

This paper proposes a recursive least squares algorithm for Wiener systems. We use a switching function to turn the modelof the nonlinear Wiener systems into an identification model, then propose a recursive least squares identification algorithm toestimate all the unknown parameters of the systems. Finally, an example is provided to show the effectiveness of the proposed algorithm.

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