RADIAL BASIS FUNCTION ARTIFICIAL NEURAL NETWORKS AND FUZZY LOGIC

Abstract

This paper examines the underlying relationship between radial basis function artificial neural networks and a type of fuzzy controller. The major advantage of this relationship is that the methodology developed for training such networks can be used to develop 'intelligent' fuzzy controlers and an application in the field of robotics is outlined. An approach to rule extraction is also described. Much of Zadeh's original work on fuzzy logic made use of the MAX/MIN form of the compositional rule of inference. A trainable/adaptive network which is capable of learning to perform this type of inference is also developed.