DocumentCode
2729301
Title
Neural Network Online Decoupling for a Class of Nonlinear System
Author
Li, Xinli ; Bai, Yan ; Yang, Lin
Author_Institution
Dept. of Autom., North China Electr. Power Univ., Beijing
Volume
1
fYear
0
fDate
0-0 0
Firstpage
2920
Lastpage
2924
Abstract
Aim at a class of nonlinear MIMO systems, the neural networks online decoupling algorithm is proposed. The elitist genetic algorithms and hybrid genetic algorithms are adopted respectively to train the neural networks in order to compensate coupling effect. Based on analysis of the convergence of the genetic algorithms, the feasibility of the online decoupling algorithm is discussed. The effectiveness of the algorithm has been shown by numerical simulations combing nonlinear MIMO system
Keywords
MIMO systems; genetic algorithms; neurocontrollers; nonlinear control systems; elitist genetic algorithms; hybrid genetic algorithms; neural network online decoupling; nonlinear MIMO systems; Automation; Control systems; Convergence; Genetic algorithms; MIMO; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Vehicle dynamics; Convergence; Genetic algorithm; Neural network; Nonlinear system; Online decoupling;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location
Dalian
Print_ISBN
1-4244-0332-4
Type
conf
DOI
10.1109/WCICA.2006.1712900
Filename
1712900
Link To Document