RT Journal Article
T1 An efficient adaptive fuzzy inference system for complex and high dimensional regression problems in linguistic fuzzy modelling
A1 Márquez Hernández, Antonio Ángel
A1 Márquez Hernández, Francisco Alfredo
A1 Roldán Ruiz, Ana María
A1 Peregrín Rubio, Antonio
AB The use of adaptive connectors as conjunction operators in adaptive fuzzy inference systems is one of the methodologies, also compatible with others, to improve the accuracy of fuzzy rule-based systems by means of local adaptation of the inference process to each rule of the rule base. However, when dealing with such currently challenging issues as high-dimensional regression problems, adapting their parameters becomes difficult due to the exponential rule explosion.In this paper, we propose to address the problem by using a new adaptive conjunction operator. This operator provides considerable advantages in efficiency while maintaining the accuracy. Moreover, it is completed with a multi-objective evolutionary algorithm as a search method due to its efficiency in achieving different balances between complexity and accuracy in the learned fuzzy systems.An in-depth experimental study is performed to show the advantages of the proposal presented, using 17 regression problems of different size and complexity, using different rule bases, analyzing the multi-objective algorithms and Pareto fronts obtained and performing statistical analyses. It confirms its effectiveness in terms of efficiency, but also in terms of accuracy and complexity of the obtained models.
PB Elsevier
SN 0950-7051
YR 2013
FD 2013-12-01
LK https://hdl.handle.net/10272/23196
UL https://hdl.handle.net/10272/23196
LA eng
NO A.A. Márquez, F.A. Márquez, A.M. Roldán, A.Peregrín. An efficient adaptive fuzzy inference system for complex and high dimensional regression problems in linguistic fuzzy modelling. Knowledge Based Systems, Vol. 54, pp. 42-52 (2013)
NO Trabajo sobre el uso de operadores difusos adaptativos parametrizados, adaptados mediante métodos evolutivos multiobjetivo, para sistemas de inferencia para aumentar la precisión de los sistemas basados en reglas difusas, en entornos de grandes conjuntos de datos.
NO Spanish Ministryof Economy and Competitiveness under Grant no. TIN2008-06681-C06-06
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026