Rule Base and Inference System Cooperative Learning of Mamdani Fuzzy Systems with Multiobjective Genetic Algorithms
نویسندگان
چکیده
In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with different trade-offs between interpretability and accuracy in linguistic fuzzy modeling, allowing the designers select the one that involves the most adequate equilibrium for the desired application. Keywords— Linguistic fuzzy modeling, interpretability-accuracy trade-off, Multiobjective genetic algorithms, adaptive Inference System, adaptive defuzzification, rule learning.
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Rule base and adaptive fuzzy operators cooperative learning of Mamdani fuzzy systems with multi-objective genetic algorithms
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