Title :
Construction of Interpretable and Precise Fuzzy Models Using Fuzzy Clustering and Multi-Objective Genetic Algorithm
Author :
Xing, Zong-yi ; Hou, Yuan-long ; Zhang, Yong ; Jia, Li-min ; Gao, Qiang
Author_Institution :
Sch. of Mech. Eng., Nanjing Univ. of Sci. & Technol., Jiangsu
Abstract :
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
Keywords :
fuzzy set theory; genetic algorithms; iterative methods; least squares approximations; pattern clustering; Mackey-Glass tine series; fuzzy clustering; fuzzy modeling; interpretability; iterative method; least square method; multiobjective genetic algorithm; Clustering algorithms; Cybernetics; Decision making; Fuzzy sets; Genetic algorithms; Input variables; Iterative algorithms; Machine learning; Mechanical engineering; Merging; Predictive models; Takagi-Sugeno model; Fuzzy clustering; Fuzzy modeling; Interpretability; Multi-objective genetic algorithm; TS fuzzy model;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.259124
