In: Proc. 8th Int. Fuzzy Systems Association World Congress (IFSA'99), 17-20 August 1999, Chungh, Taiwan, Vol. 2, pages 586-589. 1999.
Abstract: Fuzzy inference is a method to describe nonlinear input-output relationships using fuzzy if-then rules. One of the important problems of fuzzy control is stability of the fuzzy control system. The authors have applied Petri nets to the stability analysis of the fuzzy control system. A theory of asymptotic stability has been derived for the symbolic representation of the control system. The paper presents a method to bridge the stability analysis on the symbolic level to the actual behavior of the control system on the numerical level. The new method uses a generalized fuzzy Petri net model and its neural network representation. Conditions for the validity of the stability analysis of the granularized control system are derived from the movement of tokens in the neural network representation. Simulations are used to examine the derived conditions
Keywords: fuzzy Petri nets, fuzzy control systems, stability analysis.
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