Constructing Interpretable Fuzzy Model Based on Reduction Methodology
نویسندگان
چکیده
A systematic approach for constructing interpretable fuzzy model based on reduction methodology is proposed. Fuzzy clustering algorithm, combined with least square method, is used to identify initial fuzzy model with overestimated rule number. Orthogonal least square algorithm and similar fuzzy sets merging are then applied to remove redundancy of the fuzzy model. In order to obtain high accuracy, yet preserving interpretability, a constrained real coded genetic algorithm is utilized to optimize reduced fuzzy model. The proposed method was applied to automobile MPG prediction, and results show its validity. Copyright © 2005 IFAC
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