Evolutionary Learning in Identification of Fuzzy Models: Application to Damadics Benchmark
نویسندگان
چکیده
Evolutionary learning and especially genetic optimisation algorithms have recently received a lot of research attention as tools for identifying fuzzy models of the systems. Most often fuzzy modelling employ the fuzzy IF–THEN rules. In this paper, besides AND–operator the OR–operator is also considered in constructing the premise rule base. A genetic algorithm is utilised to find the premise structure of the rules, also to optimise fuzzy set membership functions and the consequent model structure of the rules at the same time. The performance of the approach is demonstrated on the DAMADICS benchmark problem.
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