SELF-TUNING FUZZY MODELING WITH ADAPTIVE MEMBERSHIP FUNCTION, RULES, AND HIERARCHICAL STRUCTURE-BASED ON GENETIC ALGORITHM

Recently, fuzzy systems have been used in many fields and places. In o rder to apply the fuzzy system to the various fields, the tuning and o ptimizing method of the fuzzy system is the key issue. Some self-tunin g 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 c onsist of some membership function expressed by the radial basis funct ion with insensitive region. Learning is carried out by the genetic al gorithms. The descent method is also utilized for tuning the shapes of the membership function and consequent parts. The effectiveness of th e proposed methods is shown by some numerical examples.