Learning Methods for Fuzzy Inference System Using Vector Quantization

Abstract

Many studies on fuzzy modeling (learning of fuzzy inference systems) with vector quantization (VQ) and steepest descend method (SDM) have been made. It is known that they are superior in the number of rules (parameters) compared with other learning methods. Most of conventional learning methods using VQ are ones that determine initial assignment of center parameters for membership functions in antecedent part using input part and, input and output part of learning data. On the other hand, the VQ learning method performing supervised learning for learning data is known. Therefore, it is desired that the learning method combining these VQ methods shows good performance. In this paper, we will propose a learning method combining VQ and SDM methods. In order to demonstrate the effectiveness of the proposed method, numerical simulations for function approximation and pattern classification problems are performed.