The pseudo outer-product based fuzzy neural network (POPFNN), and its two fuzzy-rule-identification algorithms are proposed. They are the pseudo outer-product (POP) learning and the lazy pseudo outer-product (LazyPOP) learning algorithms. These two learning algorithms are used in POPFNN to identify relevant fuzzy rules. The proposed algorithms have many advantages, such as fast, reliable, efficient, and easy to understand. POP learning is a simple one-pass learning algorithm. It essentially performs rule-selection. Hence, it suffers from the shortcoming of having to consider all the possible rules. The second algorithm, the LazyPOP learning algorithm, truly identifies the fuzzy rules which are relevant and does not use a rule-selection method whereby irrelevant fuzzy rules are eliminated from an initial rule set. In addition, it is able to adjust the structure of the fuzzy neural network. The proposed LazyPOP learning algorithm is able to delete invalid feature inputs according to the fuzzy rules that have been identified. Extensive experimental results and discussions are presented for a detailed analysis of the proposed algorithms.


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    Title :

    The POP learning algorithms: reducing work in identifying fuzzy rules


    Contributors:
    Quek, C. (author) / Zhou, R.W. (author)

    Published in:

    Neural Networks ; 14 , 10 ; 1431-1445


    Publication date :

    2001


    Size :

    15 Seiten, 31 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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