An application of fuzzy neural networks to a stability analysis of fuzzy control systems

The authors present a method for analyzing the stability of fuzzy control systems using fuzzy neural networks (FNNs). The FNNs are capable of acquiring fuzzy rules and tuning the membership functions automatically with the backpropagation (BP) learning algorithm. One FNN is used to obtain a fuzzy model of the controlled object. The other FNN is trained to acquire a fuzzy model of the controller. A new definition of the stability of fuzzy control systems is also presented. Using the fuzzy controller and the fuzzy model obtained for the controlled object, a stability analysis based on the proposed definition is done linguistically and is very easy to understand.<<ETX>>

[1]  Yoshiki Uchikawa,et al.  On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm , 1992, IEEE Trans. Neural Networks.

[2]  Madan M. Gupta,et al.  On fuzzy neuron models , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[3]  Kazuo Tanaka,et al.  Stability analysis and design of fuzzy control systems , 1992 .

[4]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Shinzo Kitamura,et al.  A Stability Condition for Fuzzy Ruled Control Systems , 1991 .