Premium
Increasing fuzzy rules cooperation based on evolutionary adaptive inference systems
Author(s) -
AlcaláFdez Jesús,
Herrera Francisco,
Márquez Francisco,
Peregrín Antonio
Publication year - 2007
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20237
Subject(s) - interpretability , adaptive neuro fuzzy inference system , computer science , inference , fuzzy control system , interpretation (philosophy) , artificial intelligence , fuzzy logic , neuro fuzzy , fuzzy rule , fuzzy inference system , rule of inference , machine learning , data mining , programming language
This article presents a study on the use of parametrized operators in the Inference System of linguistic fuzzy systems adapted by evolutionary algorithms, for achieving better cooperation among fuzzy rules. This approach produces a kind of rule cooperation by means of the inference system, increasing the accuracy of the fuzzy system without losing its interpretability. We study the different alternatives for introducing parameters in the Inference System and analyze their interpretation and how they affect the rest of the components of the fuzzy system. We take into account three applications in order to analyze their accuracy in practice. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1035–1064, 2007.