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Modelling with Words pp 44–63Cite as

Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2873))

Abstract

In complex multidimensional problems with a highly nonlinear input-output relation, inconsistent or redundant rules can be found in the fuzzy model rule base, which can result in a loss of accuracy and interpretability. Moreover, the rules could not cooperate in the best possible way.

It is known that the use of rule weights as a local tuning of linguistic rules, enables the linguistic fuzzy models to cope with inefficient and/or redundant rules and thereby enhances the robustness, flexibility and system modeling capability. On the other hand, rule selection performs a simplification of the previously identified fuzzy rule base, removing inefficient and/or redundant rules in order to improve the cooperation among them. Since both approaches are not isolated and they have complementary characteristics, they could be combined among them. In this work, we analyze the hybridization of both techniques to obtain simpler and more accurate linguistic fuzzy models.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Jaén, E-23071, Jaén, Spain

    Rafael Alcalá

  2. Department of Computer Science and A.I., University of Granada, E-18071, Granada, Spain

    Oscar Cordón & Francisco Herrera

Authors
  1. Rafael Alcalá
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  2. Oscar Cordón
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  3. Francisco Herrera
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Editor information

Editors and Affiliations

  1. Artificial Intelligence Group, Engineering Mathematics Department, University of Bristol, BS8 1TR, UK

    Jonathan Lawry

  2. Clairvoyance Corporation, 5301 Fifth Avenue, 15232, Pittsburgh, PA, USA

    Jimi Shanahan

  3. ECECS Department, University of Cincinnati, ML 0030, 45221-0030, Cincinnati, OH, USA

    Anca L. Ralescu

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© 2003 Springer-Verlag Berlin Heidelberg

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Alcalá, R., Cordón, O., Herrera, F. (2003). Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models. In: Lawry, J., Shanahan, J., L. Ralescu, A. (eds) Modelling with Words. Lecture Notes in Computer Science(), vol 2873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39906-3_3

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  • DOI: https://doi.org/10.1007/978-3-540-39906-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20487-9

  • Online ISBN: 978-3-540-39906-3

  • eBook Packages: Springer Book Archive

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