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Rule Base Reduction and Genetic Tuning of Fuzzy Systems Based on the Linguistic 3-tuples Representation

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Abstract

Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation model, that allows the symbolic translation of a label considering an unique parameter. It involves a reduction of the search space that eases the derivation of optimal models. This work presents a new symbolic representation with three values (s, α, β), respectively representing a label, the lateral displacement and the amplitude variation of the support of this label. Based on this new representation we propose a new method for fine tuning of membership functions that is combined with a rule base reduction method in order to extract the most useful tuned rules. This approach makes use of a modified inference system that consider non-covered inputs in order to improve the final fuzzy model generalization ability, specially in highly non-linear problems with noise points. Additionally, we analyze the proposed approach showing its behavior in two real-world applications.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Rafael Alcalá, Jesús Alcalá-Fdez, María José Gacto & Francisco Herrera

Authors
  1. Rafael Alcalá
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  2. Jesús Alcalá-Fdez
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  3. María José Gacto
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  4. Francisco Herrera
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Corresponding author

Correspondence to Rafael Alcalá.

Additional information

Supported by the Spanish Ministry of Science and Technology under Projects TIC-2002-04036-C05-01 and TIN-2005-08386-C05-01.

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Alcalá, R., Alcalá-Fdez, J., Gacto, M.J. et al. Rule Base Reduction and Genetic Tuning of Fuzzy Systems Based on the Linguistic 3-tuples Representation. Soft Comput 11, 401–419 (2007). https://doi.org/10.1007/s00500-006-0106-2

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