Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition

被引：117

作者：

Ding, Feng ^{[1
]}

Wang, Xuehai ^{[1
]}

Chen, Qijia ^{[1
]}

Xiao, Yongsong ^{[1
]}

机构：

[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China

来源：

CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2016年 / 35卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Parameter estimation; Recursive identification; Least squares; Hierarchical identification principle; Nonlinear system; MOVING AVERAGE SYSTEMS; STOCHASTIC GRADIENT ALGORITHM; IDENTIFICATION ALGORITHM; ITERATIVE ESTIMATION; HAMMERSTEIN SYSTEMS; FILTERING TECHNIQUE; WIENER SYSTEMS; COLORED NOISE;

D O I：

10.1007/s00034-015-0190-6

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we study the parameter estimation problem of a class of output nonlinear systems and propose a recursive least squares (RLS) algorithm for estimating the parameters of the nonlinear systems based on the model decomposition. The proposed algorithm has lower computational cost than the existing over-parameterization model-based RLS algorithm. The simulation results indicate that the proposed algorithm can effectively estimate the parameters of the nonlinear systems.

引用

页码：3323 / 3338

页数：16

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