DocumentCode :
1717335
Title :
GA based fuzzy neural network generalized predictive control method
Author :
Tao Ji-Li ; Wang Ning ; Zhang Ri-dong
Author_Institution :
Ningbo Inst. of Technol., Zhejiang Univ., Ningbo, China
fYear :
2013
Firstpage :
4062
Lastpage :
4067
Abstract :
A recurrent fuzzy neural network (RFNN) modeling based on genetic algorithm is designed to control the pH neutralization nonlinear process by generalized predictive controller (GPC). GA is used to optimize the number of fuzzy rules, the centers and widths of Gaussion membership function. The recurrent least square method is utilized to obtain the weights of sequent part in the RFNN. Thus, the dynamic RFNN model is obtained with high precision to pH neutralization process. The linearized model at every control period is derived and the nonlinear optimization problem in generalized predictive control is simplified. Simulation results of pH neutralization process show that the proposed method overcome the complexity of neural network based generalized predictive control, and ensure the control precision and robustness.
Keywords :
Gaussian processes; fuzzy control; genetic algorithms; neurocontrollers; nonlinear control systems; pH control; predictive control; recurrent neural nets; GA; Gaussion membership function; RFNN modeling; fuzzy neural network generalized predictive control method; fuzzy neural network modeling; fuzzy rules; genetic algorithm; nonlinear optimization problem; pH neutralization nonlinear process control; recurrent least square method; Educational institutions; Fuzzy control; Fuzzy neural networks; Genetic algorithms; Predictive control; Predictive models; Genetic algorithm; generalized predictive control; pH neutralization process; recurrent fuzzy neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an
Type :
conf
Filename :
6640131
Link To Document :
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