scholarly journals Recursive Least-Squares Estimation for Hammerstein Nonlinear Systems with Nonuniform Sampling

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiangli Li ◽  
Lincheng Zhou ◽  
Ruifeng Ding ◽  
Jie Sheng
Keyword(s):  
Nonlinear Systems ◽  
Least Squares ◽  
Identification Problem ◽  
Nonuniform Sampling ◽  
Recursive Least Squares ◽  
Separation Principle ◽  
Output Data ◽  
Auxiliary Model ◽  
Information Vector ◽  
Identification Model

This paper focuses on the identification problem of Hammerstein nonlinear systems with nonuniform sampling. Using the key-term separation principle, we present a discrete identification model with nonuniform sampling input and output data based on the frame period. To estimate parameters of the presented model, an auxiliary model-based recursive least-squares algorithm is derived by replacing the unmeasurable variables in the information vector with their corresponding recursive estimates. The simulation results show the effectiveness of the proposed algorithm.

scholarly journals Least-Squares-Based Iterative Identification Algorithm for Wiener Nonlinear Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Lincheng Zhou ◽  
Xiangli Li ◽  
Feng Pan
Keyword(s):  
Nonlinear Systems ◽  
Least Squares ◽  
Iterative Algorithm ◽  
Identification Problem ◽  
Identification Algorithm ◽  
Separation Principle ◽  
Iterative Identification ◽  
Simulation Results ◽  
Information Vector ◽  
Simplified Form

This paper focuses on the identification problem of Wiener nonlinear systems. The application of the key-term separation principle provides a simplified form of the estimated parameter model. To solve the identification problem of Wiener nonlinear systems with the unmeasurable variables in the information vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables in the information vector with their corresponding iterative estimates. The simulation results indicate that the proposed algorithm is effective.

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.

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