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.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    The POP learning algorithms: reducing work in identifying fuzzy rules


    Beteiligte:
    Quek, C. (Autor:in) / Zhou, R.W. (Autor:in)

    Erschienen in:

    Neural Networks ; 14 , 10 ; 1431-1445


    Erscheinungsdatum :

    2001


    Format / Umfang :

    15 Seiten, 31 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    Tuning Fuzzy Control Rules via Genetic Algorithms

    Lagunas-Jimenez, Ruben / Pitalua-Diaz, Nun | IEEE | 2007


    Learning fuzzy rules with tabu search-an application to control

    Denna, M. / Mauri, G. / Zanaboni, A.M. | Tema Archiv | 1999


    Q-learning with Condition Reduced Fuzzy Rules and Its Applications

    Yamagishi, H. / Kawakami, H. / Horiuchi, T. et al. | British Library Online Contents | 2001


    HYBRID GENETIC-FUZZY ALGORITHMS FOR IDENTIFYING HUMAN PILOT CUE UTILIZATION FROM SIMULATION AND FLIGHT DATA

    Zeyada, Y. / Hess, R. A. / Heffley, R. K. et al. | British Library Conference Proceedings | 2001


    Acquisition of intelligible fuzzy rules

    Onisawa, T. / Anzai, T. | Tema Archiv | 1999