Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm

This paper proposes a new supervised self-tuning fuzzy modeling, which consist of some membership function expressed by the radial basis function with insensitive region with descent method, which is carried out by the genetic algorithms.

Abstract

Recently, fuzzy systems have been used in many fields and places. In order to apply the fuzzy system to the various fields, the tuning and optimizing method of the fuzzy system is the key issue. Some self-tuning methods have been proposed so far. However, these conventional self-tuning methods do not have sufficient capability of learning. In this paper, we propose a new supervised self-tuning fuzzy modeling, which consist of some membership function expressed by the radial basis function with insensitive region. Learning is carried out by the genetic algorithms. The descent method is also utilized for tuning the shapes of the membership function and consequent parts. The effectiveness of the proposed methods is shown by some numerical examples.