Fuzzy Clustering - Principles, Methods and Examples

Uri Kroszynski, Jianjun Zhou

    Research output: Book/ReportReport

    Abstract

    One of the most remarkable advances in the field of identification and control of systems -in particular mechanical systems- whose behaviour can not be described by means of the usual mathematical models, has been achieved by the application of methods of fuzzy theory.In the framework of a study about identification of "black-box" properties by analysis of system input/output data sets, we have prepared an introductory note on the principles and the most popular data classification methods used in fuzzy modeling. This introductory note also includes some examples that illustrate the use of the methods. The examples were solved by hand and served as a test bench for exploration of the MATLAB capabilities included in the Fuzzy Control Toolbox. The fuzzy clustering methods described include Fuzzy c-means (FCM), Fuzzy c-lines (FCL) and Fuzzy c-elliptotypes (FCE).
    Original languageEnglish
    Number of pages13
    Publication statusPublished - 1998

    Cite this

    Kroszynski, U., & Zhou, J. (1998). Fuzzy Clustering - Principles, Methods and Examples.