Learning Algorithms for Fuzzy Inference Systems Using Vector Quantization

Many studies on learning of fuzzy inference systems have been made. Specifically, it is known that learning methods using vector quantization (VQ) and steepest descent method (SDM) are superior to other methods. In their learning methods, VQ is used only in determination of the initial parameters for the antecedent part of fuzzy rules. In order to improve them, some methods determining the initial parameters for the consequent part by VQ are proposed. For example, learning method composed of three stages as VQ, generalized inverse matrix (GIM), and SDM was proposed in the previous paper. In this paper, we will propose improved methods for learning process of SDM for learning methods using VQ, GIM, and SDM and show that the methods are superior in the number of rules to the conventional methods in numerical simulations.