Abstract
Fuzzy reasoning models are model-free estimators of control systems, which makes them very powerful tools in control applications. Their performances depend on several factors such as the completeness of the rule base, the subjective definitions of the fuzzy subset parameters or fuzzy partitioning, the fuzzy implication operator and the defuzzification method. In most cases these factors are decided by the subjective experience of the human expert. The authors attempt to improve the fuzzy systems performances by means of a genetic-based learning mechanism. They propose a new methodology based upon two genetic algorithm (GA) loops; the inner GA loop enables the generation of an optimal set of parameters of the fuzzy reasoning matrix based upon their selection by particular optimal fuzzy partitioning generated by an outer GA loop. Simulation of typical control problems shows the effectiveness and high performance of such a method. The effects of GA parameter settings on convergence and total performance are also discussed.
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