The bi-objective fuzzy c-means cluster analysis for TSK fuzzy system identification |
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Authors: | Ta-Wei Hung |
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Institution: | (1) Department of Information Management, Shih Chien University, Taipei, 10462, Taiwan, ROC |
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Abstract: | For conventional fuzzy clustering-based approaches to fuzzy system identification, a fuzzy function is used for cluster formation
and another fuzzy function is used for cluster validation to determine the number and location of the clusters which define
IF parts of the rule base. However, the different fuzzy functions used for cluster formation and validation may not indicate
the same best number and location of the clusters. This potential disparity motivates us to propose a new fuzzy clustering-based
approach to fuzzy system identification based on the bi-objective fuzzy c-means (BOFCM) cluster analysis. In this approach,
we use the BOFCM function for both cluster formation and validation to simultaneously determine the number and location of
the clusters which we hope can efficiently and effectively define IF parts of the rule base. The proposed approach is validated
by applying it to the truck backer-upper problem with an obstacle in the center of the field. |
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Keywords: | Fuzzy system Fuzzy system identification Fuzzy clustering Fuzzy cluster analysis Learning |
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