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
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;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1712900
