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    Proposing two defuzzification methods based on output fuzzy set weights

    Access Status
    Fulltext not available
    Authors
    Amini, A.
    Nikraz, Navid
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Amini, A. and Nikraz, N. 2016. Proposing two defuzzification methods based on output fuzzy set weights. International Journal of Intelligent Systems and Applications. 8 (2): pp. 1-12.
    Source Title
    International Journal of Intelligent Systems and Applications
    DOI
    10.5815/ijisa.2016.02.01
    ISSN
    2074-904X
    School
    Department of Civil Engineering
    URI
    http://hdl.handle.net/20.500.11937/52132
    Collection
    • Curtin Research Publications
    Abstract

    Defuzzification converts the final fuzzy output set of fuzzy controller and fuzzy inference systems to a significant crisp value. However, there are various mathematical methods for defuzzification, but there is not any certain systematic method for choosing the best strategy. In this paper, first we explain the structure of a fuzzy inference system and then after a short review of defuzzification criteria and properties, the main classification groups of most widely used defuzzification methods are presented. In the following after discussing some existing techniques, two new defuzzification methods are proposed by presenting their general performance and computational formulas. However, the principle of these two methods is using weights associated with output fuzzy set like WFM or QM, but unlike the existing approaches, they consider the final aggregated consequent and implicated functions simultaneously to calculate the weights. To show how the proposed methods act, two numerical examples are solved using the presented methods and the results are compared with some of common defuzzification techniques.

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