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

被引：47
|
作者：
Zhou, Lincheng [1 ]
Li, Xiangli [1 ,2 ]
Pan, Feng [1 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Jiangsu Coll Informat Technol, Wuxi 214153, Peoples R China
来源：
JOURNAL OF APPLIED MATHEMATICS | 2013年
基金：
中国国家自然科学基金;
关键词：
PARAMETER-ESTIMATION; HIERARCHICAL IDENTIFICATION; STOCHASTIC-SYSTEMS; CONVERGENCE;
D O I：
10.1155/2013/565841
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.
引用
收藏
页数：6
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