Parameter estimation for nonlinear dynamical adjustment models

被引：43
|
作者：
Xiao, Yongsong [1 ]
Yue, Na [1 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Sch Internet Things Engn, Wuxi 214122, Peoples R China
来源：
MATHEMATICAL AND COMPUTER MODELLING | 2011年 / 54卷 / 5-6期
基金：
中国国家自然科学基金;
关键词：
Least squares; Parameter estimation; Recursive identification; Hammerstein model; Nonlinear system; IDENTIFICATION METHODS; LEAST-SQUARES; ESTIMATION ALGORITHM; ARMAX SYSTEMS;
D O I：
10.1016/j.mcm.2011.04.027
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A recursive generalized least squares algorithm and a filtering based least squares algorithm are developed for input nonlinear dynamical adjustment models with memoryless nonlinear blocks followed by linear dynamical blocks. The basic idea is to use the filtering technique and to replace the unknown terms in the information vectors with their estimates. The simulation results show the performance of the proposed algorithms. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1561 / 1568
页数：8
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