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