Abstract
Fuzzy neural controllers have the advantages of ease for knowledge expression and the ability of self-learning, and are able to control adaptively by updating the fuzzy rules and the membership functions. Nevertheless, the long training time usually discourages their practical applications in industry and the parameters over-updating may make system oscillate extensively. In this paper, a new strategy for optimizing the parameters updating algorithm of fuzzy neural controller is proposed. The only effect of parameters which affects the control performance significantly are updated. Also, based on fuzzy inference, the updating step is adjusted adaptively in accordance with the error and the change of error of the system. Two examples are simulated in order to conform the effectiveness and applicability of the strategy proposed in this paper.
Similar content being viewed by others
References
Chen CT, Peng ST (1999) Intelligent process control using neural fuzzy techniques. J Process Control 9:493–503
Leu YG, Lee TT,Wang WY (1997) On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems. IEEE Trans Syst Man Cybern 6:1034–1043
Shi Y, Mizumoto M (2000) Some considerations on conventional neural-fuzzy learning algorithms by gradient descent method. Fuzzy Sets Syst 112:51–63
Shi Y, Mizumoto M (2001) An improvement of neural-fuzzy learning algorithm for tuning fuzzy rules. Fuzzy Sets Syst 118:339–350
Stoeva S, Nikov A (2000) A fuzzy backpropagation algorithm. Fuzzy Sets Syst 112:27–39
Sun ZQ (1990) Intelligent control theory and technology. Tsinghua University press
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Man Syst Cybern 15:116–132
Acknowledgments
The financial support from the Science Hall of Shan Xi province (2003F33) is acknowledged.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, J., Liu, D., Bai, HY. et al. A new strategy for optimizing the parameters updating algorithm of fuzzy neural controller. Soft Comput 10, 61–67 (2006). https://doi.org/10.1007/s00500-005-0467-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-005-0467-y