A three-part input-output clustering-based approach to fuzzy system identification

Shin Jye Lee, Xiao Jun Zeng

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing an effective initial fuzzy model. Due to the above reasons, a hybrid clustering algorithm concerning input, output, generalization and specialization has hence been introduced in this article. Further, the proposed clustering technique, three-part input-output clustering algorithm, integrates a variety of clustering features simultaneously, including the advantages of input clustering, output clustering, flat clustering, and hierarchical clustering, to effectively perform the identification of clustering problem. © 2010 IEEE.
    Original languageEnglish
    Title of host publicationProceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10|Proc. Int. Conf. Intelligent Syst. Des. Appl., ISDA
    PublisherIEEE
    Pages55-60
    Number of pages5
    ISBN (Print)9781424481354
    DOIs
    Publication statusPublished - 2010
    Event2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 - Cairo
    Duration: 1 Jul 2010 → …

    Conference

    Conference2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
    CityCairo
    Period1/07/10 → …

    Keywords

    • Fuzzy set
    • Fuzzy system identification
    • Hybrid clustering

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