Title :
Fuzzy neural PID controller and tuning its weight factors using genetic algorithm based on different location crossover
Author :
Yongquan, Yu ; Ying, Huang ; Minghui, Wring ; Bi, Zeng ; Guokun, Zhong
Author_Institution :
Inst. of Intelligent Eng., Guangdong Univ. of Technol., Guangzhou, China
Abstract :
The new method using genetic algorithm to modify the weight factors of PID neural network (PIDNN) in fuzzy neural PID controller is presented in this paper. The genetic algorithm uses the new crossover operator, this is the different location crossover, to carry) out the evolutionary operating. The principle of different crossover operators is described and the neural fuzzy PID controller used to the processing control system. The result of running shows that the fuzzy neural PID controller optimized by genetic algorithm has the better and satisfactory behavior for real time industrial control processing.
Keywords :
fuzzy control; genetic algorithms; neurocontrollers; three-term control; fuzzy neural PID control; genetic algorithm; location crossover operator; processing control system; weight factors; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Genetic algorithms; Neural networks; Neurons; Pi control; Process control; Proportional control; Three-term control;
Conference_Titel :
Systems, Man and Cybernetics, 2004 IEEE International Conference on
Print_ISBN :
0-7803-8566-7
DOI :
10.1109/ICSMC.2004.1400920