Content area
Abstract
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.
Details
Title
Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition
Author
Ding, Feng 1 ; Wang, Xuehai 1 ; Chen, Qijia 1 ; Xiao, Yongsong 1
1 Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, People’s Republic of China
1 Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, People’s Republic of China
Pages
3323-3338
Publication year
2016
Publication date
Sep 2016
ISSN
0278081X
e-ISSN
15315878
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2135713677
Copyright
Circuits, Systems, and Signal Processing is a copyright of Springer, (2015). All Rights Reserved.