Abstract
This paper concerns a fuzzy set-based fuzzy system formed by using isolated fuzzy spaces (fuzzy set) and its related two methodologies of fuzzy identification. This model implements system structure and parameter identification by means of information granulation and genetic algorithms. Information granules are sought as associated collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Information granulation realized with HCM clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise and the initial values of coefficients of polynomial function located in the consequence. And the initial parameters are tuned by means of the genetic algorithms and the least square method. To optimally identify the structure and parameters of fuzzy model we exploit two design methodologies such as a separative and a consecutive identification for tuning of the fuzzy model using genetic algorithms. The proposed model is contrasted with the performance of the conventional fuzzy models presented previously in the literature.
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Oh, SK., Park, KJ., Pedrycz, W. (2006). Design Methodologies of Fuzzy Set-Based Fuzzy Model Based on GAs and Information Granulation. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_14
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DOI: https://doi.org/10.1007/11941439_14
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