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North American Fuzzy Information Processing Society Annual Conference

NAFIPS 2017: Fuzzy Logic in Intelligent System Design pp 162–171Cite as

Evolving Granular Fuzzy Min-Max Regression

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 648))

Abstract

This paper suggests an evolving granular min-max regression algorithm for fuzzy rule-based system modeling. The algorithm starts with an empty rule base, but adds or modifies the rule base as stream data are input. Granulation of data is done by partitioning the input space using hyperboxes and associating to each hyperbox a fuzzy set and a fuzzy functional rule with affine consequent. The model output is produced combining the affine consequents weighted by the normalized membership degrees of the active fuzzy rules. The parameters of the consequents are adjusted using the recursive least squares with a forgetting factor. The algorithm has an incremental nature, and learns with one-pass processing of the data. The recursive form of the algorithm allows gradual model changes using simple maximum, minimum, and comparison operations, an appealing feature when handling high-dimensional data. Computational experiments concerning time series forecasting and system identification show that the evolving granular fuzzy min-max algorithm is fast, memory efficient, and competitive with current state of the art approaches.

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Acknowledgement

The authors thank the Brazilian National Council for Scientific and Technological Development (CNPq) for a fellowship, and grant 305906/2014-3, respectively.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil

    Alisson Porto & Fernando Gomide

Authors
  1. Alisson Porto
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  2. Fernando Gomide
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Corresponding author

Correspondence to Alisson Porto .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Reformat

  5. Laboratory of Computational Intelligence and Automation, University of Waterloo, Waterloo, Ontario, Canada

    William Melek

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Porto, A., Gomide, F. (2018). Evolving Granular Fuzzy Min-Max Regression. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-67137-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67136-9

  • Online ISBN: 978-3-319-67137-6

  • eBook Packages: EngineeringEngineering (R0)

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