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A Cluster Analysis Approach for Rule Base Reduction

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Neural Advances in Processing Nonlinear Dynamic Signals (WIRN 2017 2017)

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Abstract

In this paper we propose an iterative algorithm for fuzzy rule base simplification based on cluster analysis. The proposed approach uses a dissimilarity measure that allows to assign different importance to values and ambiguities of fuzzy terms in antecedent and consequent parts of fuzzy rules.

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Author information

Authors and Affiliations

  1. Department of Management, Economics, Mathematics and Statistics, University of Salento, Lecce, Italy

    Luca Anzilli

  2. Department of Economics, University Ca’ Foscari of Venice, Venice, Italy

    Silvio Giove

Authors
  1. Luca Anzilli
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  2. Silvio Giove
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Corresponding author

Correspondence to Luca Anzilli .

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia, Università della Campana Luigi Vanvitelli, Caserta, Italy

    Anna Esposito

  2. Fundació Tecnocampus, Pompeu Fabra University, Mataro, Barcelona, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Laboratorio di Neuronica, Dipartimento Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Anzilli, L., Giove, S. (2019). A Cluster Analysis Approach for Rule Base Reduction. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Advances in Processing Nonlinear Dynamic Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-319-95098-3_28

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