Genetic Algorithm with Gradient Based Tuning for Constructing Fuzzy Rules
نویسندگان
چکیده
This paper presents a hybrid method to construct concise and comprehensible fuzzy rules from training data. The construction procedure consists of a genetic algorithm, which determines the rulebase, and a gradient based optimization for the tuning of the membership functions. An approximation of the well-known Lukasiewicz logic is used to describe both the fuzzy connectives and the memberships, which by this way have continuous derivatives.
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