A general method is developed for generating fuzzy rules from numerical data. The method consists of five steps: dividing the input and output spaces of the given numerical data into fuzzy regions; generating fuzzy rules from the given data; assigning a degree to each of the generated rules for the purpose of resolving conflicts among the generated rules; creating a combined fuzzy-associative-memory (FAM) bank based on both the generated rules and linguistic rules of human experts; and determining a mapping from input space to output space based on the combined FAM bank using a defuzzifying procedure. The mapping is proved to be capable of approximating any real continuous function on a compact set to arbitrary accuracy. The method is applied to predicting a chaotic time series.<>


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    Title :

    Generating fuzzy rules by learning from examples


    Contributors:
    Wang, L.-X. (author) / Mendel, J.M. (author)


    Publication date :

    1991-01-01


    Size :

    630201 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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