Parallel Distributed Two-Level Evolutionary Multiobjective Methodology for Granularity Learning and Membership Functions Tuning in Linguistic Fuzzy Systems

Authors:
Miguel A. De Vega;Juan M. Bardallo;Francisco A. Márquez;Antonio Peregrín
Affiliations:
-;-;-;-
Venue:
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Year:
2009

Citing 0
Cited 2

A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems

International Journal of Approximate Reasoning
An efficient adaptive fuzzy inference system for complex and high dimensional regression problems in linguistic fuzzy modelling

Knowledge-Based Systems

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Abstract

This paper deals with the learning of the membership functions for Mamdani Fuzzy Systems – the number of labels of the variables and the tuning of them – in order to obtain a set of Linguistic Fuzzy Systems with different trade-offs between accuracy and complexity, through the use of a two-level evolutionary multi-objective algorithm. The presented methodology employs a high level main evolutionary multi-objective heuristic searching the number of labels, and some distributed low level ones, also evolutionary, tuning the membership functions of the candidate variable partitions.