Treffer: Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms

Title:
Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms
Authors:
GONZALEZ, Jesus, ROJAS, Ignacio, POMARES, Héctor, HERRERA, Luis J, GUILLEN, Alberto, PALOMARES, José M, ROIAS, Fernando
Source:
Genetic fuzzy systems and the interpretability-accuracy trade-offInternational journal of approximate reasoning. 44(1):32-44
Publisher Information:
Amsterdam: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 49 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Ajustement modèle, Model matching, Ajustamiento modelo, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Approximation fonction, Function approximation, Ensemble flou, Fuzzy set, Conjunto difuso, Fonction appartenance, Membership function, Función pertenencia, Identification système, System identification, Identificación sistema, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Logique floue, Fuzzy logic, Lógica difusa, Modélisation, Modeling, Modelización, Optimisation, Optimization, Optimización, Optimum Pareto, Pareto optimum, Optimo Pareto, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Système construction, Construction system, Sistema construcción, Transparence, Transparency, Transparencia, Evolutionary algorithms, Fuzzy systems, Multi-objective optimization
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Architecture and Computer Technology, E.T.S. Ingenierías Informática y de Telecomunicacíon, University of Granada, Granada 18071, Spain
ISSN:
0888-613X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18470712
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18470712
Database:
PASCAL Archive

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The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. An important characteristic that distinguishes fuzzy systems from other techniques in this area is their transparency and interpretability. Especially in the construction of a fuzzy system from a set of given training examples, little attention has been paid to the analysis of the trade-off between complexity and accuracy maintaining the interpretability of the final fuzzy system. In this paper a multi-objective evolutionary approach is proposed to determine a Pareto-optimum set of fuzzy systems with different compromises between their accuracy and complexity. In particular, two fundamental and competing objectives concerning fuzzy system modeling are addressed: fuzzy rule parameter optimization and the identification of system structure (i.e. the number of membership functions and fuzzy rules), taking always in mind the transparency of the obtained system. Another key aspect of the algorithm presented in this work is the use of some new expert evolutionary operators, specifically designed for the problem of fuzzy function approximation, that try to avoid the generation of worse solutions in order to accelerate the convergence of the algorithm.