Nuevas propuestas en el ámbito de los operadores adaptativos para Sistemas Difusos Lingüísticos Evolutivos Multiobjetivo

Abstract

This research develops new learning and adjustment proposals in the field of operators used into adaptive fuzzy Inference System and defuzzification interface for Linguistic Multiobjective Evolutionary Fuzzy Systems. For this purpose different learning algorithms for obtain precise and compact Fuzzy Rule Based Systems have been designed and presented. Specifically, models with good trade-off between accuracy and interpretability are proposed, we progress in the use of measures and design indexes to quantify and better optimize interpretability, and methods to address the design of fuzzy systems based on data sets with high dimensional and / or size are proposed.