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Automatic Optimal Design of Fuzzy Systems based on Universal Approximation and Evolutionary Programming

A strategy for automatic optimal fuzzy system design is proposed and investgated. The fuzzy system chosen has been shown to be an universal approximator. The optimization technique is the algorithm Guided Evolutionary Simulated Annealing (GESA). It has strong similarities with a parallel simulated annealing algorithm which has been shown to be able to find any global optimum. The proposed design strategy is applied to two different problems: general function estimation in the form of the "generalized parity-2 problem" and control of an inverted pendulum. Good results are obtained and in the function estimation problem, it is shown experimentally that the approximation error decreases when the fuzzy partition increases. However parts of the design strategy are very computationally intensive. Because of the complexity of the GESA algorithm, it is suggested that more experience with GESA needs to be acquired and it would be desirable to make a simplification of GESA.

Mattias Nyberg


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