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

Authors:
Rafael Alcalá;Jesús Alcalá-Fdez;María José Gacto;Francisco Herrera
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
Year:
2006

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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.