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Simplifying a neuro-fuzzy model

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Abstract

Neuro-fuzzy modeling allows a fuzzy system to be refined by neural training, thus avoiding lenghty trial-and-error phases in defining both membership functions and inference rules. An approach to obtain simple neuro-fuzzy models is proposed, which reduces the number of rules by means of a systematic procedure that consists in successively removing a rule and updating the remaining rules in such a way that the overall input-output behavior is kept approximately unchanged over the entire training set. A formulation of the proper update is described and a criterion for choosing the rules to be removed is also provided. Initial experimental results show the effectiveness of the proposed method in reducing the complexity of a neuro-fuzzy system by using its input-output data.

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Authors and Affiliations

  1. Istituto Elaborazione Segnali ed Immagini, C.N.R., Via Amendola 166/5, 70126, Bari, Italy

    G. Castellano

  2. Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona 4, 70126, Bari, Italy

    A. M. Fanelli

Authors
  1. G. Castellano
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  2. A. M. Fanelli
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Correspondence to A. M. Fanelli.

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Castellano, G., Fanelli, A.M. Simplifying a neuro-fuzzy model. Neural Process Lett 4, 75–81 (1996). https://doi.org/10.1007/BF00420616

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  • DOI: https://doi.org/10.1007/BF00420616

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