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Nefti-Meziani, S; Kaymak, U; Oussalah, M
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Languages: English
Types: Article
Subjects: other, QA75

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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