RADIAL BASIS FUNCTION ARTIFICIAL NEURAL NETWORKS FOR THE INFERENCE PROCESS IN FUZZY-LOGIC BASED CONTROL

This paper illustrates the fuzzy logic based approach to the control of a plant or a system, and discusses some of the possible shortcomings of the usual inference mechanisms. Radial basis function artificial neural networks have been shown to be effective in a number of applications, and have the advantage that network training is a very rapid process due to their structure. In fact, this is usually accomplished by the solution of a system of linear equations, a process for which fast and reliable algorithms are available. Radial basis function networks are shown to provide a means of constructing an 'inference engine' capable of handling a rule base in which plant state and control actions are specified in terms of fuzzy sets. The resulting inference mechanism is shown to avoid the phenomena of 'rule overlap' which can be a feature of fuzzy control algorithms. It is interesting to note that in this application of radial basis function networks, the usual problems on the number and location of centres do not arise. The paper concludes with a brief discussion of some experimental results achieved.