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
1994
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Last updated: 2021-11-10
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