• 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