Abstract
A new evolutionary optimization scheme for designing a Takagi-Sugeno fuzzy model is proposed in this paper. To achieve better modeling performance, asymmetric RBF membership functions are used. Penalty function is proposed and used in the fitness function to prevent overlapping membership functions in the resulting fuzzy model. The simplified fitness sharing scheme is used to enhance the searching capability of the proposed evolutionary optimization algorithm. Some simulations are performed to show the effectiveness of the proposed algorithm.
This work is partially supported by KOSEF under the Korea-Japan basic scientific promotion program and by HWRS-ERC
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Kim, MS., Kim, CH., Lee, JJ. (2003). Evolutionary Optimization of Fuzzy Models with Asymmetric RBF Membership Functions Using Simplified Fitness Sharing. In: Bilgiç, T., De Baets, B., Kaynak, O. (eds) Fuzzy Sets and Systems — IFSA 2003. IFSA 2003. Lecture Notes in Computer Science, vol 2715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44967-1_75
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DOI: https://doi.org/10.1007/3-540-44967-1_75
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