Data filtering-based least squares iterative algorithm for Hammerstein nonlinear systems by using the model decomposition

被引：0
|
作者：
Junxia Ma
Feng Ding
Erfu Yang
机构：
[1] Jiangnan University,Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)
[2] University of Strathclyde,Department of Design, Manufacture and Engineering Management, Space Mechatronic Systems Technology Laboratory, Strathclyde Space Institute
来源：
Nonlinear Dynamics | 2016年 / 83卷
关键词：
Data filtering; Least squares; Iterative algorithm ; Model decomposition; Nonlinear system;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition-based least squares iterative algorithm is presented for estimating the parameter vector in each subsystem. Moreover, a data filtering-based decomposition least squares iterative algorithm is proposed. The simulation results indicate that the data filtering-based least squares iterative algorithm can generate more accurate parameter estimates than the least squares iterative algorithm.
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页码：1895 / 1908
页数：13
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