Title :
Identification and design of multivariable fuzzy neural network system
Author :
Hongwei, Yao ; Xiaorong, Mei ; Xianyi, Zhuang
Author_Institution :
Dept. of Control Eng., Harbin Inst. of Technol., China
Abstract :
A new method of fuzzy neural network identification is proposed. A function for measuring cluster validity is defined with which the number of fuzzy rules can be determined. A sufficient criterion that guarantees the global stability of the fuzzy system is presented. Based on this, a design method to optimize parameters of the fuzzy neural network controller by genetic algorithms is presented. This method is proved to have better effect through a double inverted pendulum by experiments
Keywords :
fuzzy neural nets; genetic algorithms; identification; multivariable systems; neurocontrollers; pendulums; stability; cluster validity; fuzzy neural network; fuzzy rules; genetic algorithms; global stability; identification; inverted pendulum; multivariable system; neurocontrol; Control engineering; Design methodology; Design optimization; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Neural networks; Stability criteria;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.862989
