Construction of Interpretable and Precise Fuzzy Models Using Fuzzy Clustering and Multi-Objective Genetic Algorithm
An approach to construct interpretable and precise fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. Then a modified fuzzy clustering algorithm, combined with the least square method, is used to identify the initial fuzzy model. Third, the multi-objective genetic algorithm and interpretability-driven simplification techniques are proposed to evolve the initial fuzzy model to optimize its structure and parameters iteratively, thus interpretability and precision of the fuzzy model are improved. Finally, the proposed approach is applied to the Mackey-Glass tine series, and the results show its validity