Least squares based and gradient based iterative identification for Wiener nonlinear systems

2011 ◽  
Vol 91 (5) ◽  
pp. 1182-1189 ◽  
Author(s):  
Dongqing Wang ◽  
Feng Ding
Keyword(s):  
Nonlinear Systems ◽  
Least Squares ◽  
Iterative Identification ◽  
Gradient Based

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 The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jiling Ding
Keyword(s):  
Least Squares ◽  
Identification Problem ◽  
Parameter Estimates ◽  
Identification Algorithm ◽  
Input Output ◽  
Iterative Identification ◽  
Hierarchical Identification ◽  
Output Error ◽  
Gradient Based ◽  
Simulation Results

This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

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