Enhancing neural control systems by fuzzy logic and evolutionary reinforcement

Neural networks are widely used for system modelling and control because of their ability to approximate complex non-linear functions. Fuzzy systems, similarly, have been shown to be able to approximate or model any nonlinear system. Fuzzy-logic and neural systems, however, have very contrasting application requirements and it has been said that their integration offers a facility to bridge symbolic knowledge processing and connectionist learning. The significance of the integration becomes more apparent by considering their disparities. Neural networks do not provide a strong scheme for knowledge representation, while fuzzy systems do not possess capabilities for automated learning. On the other hand, another learning method has emerged recently, as an alternative to inductive techniques used with neural networks, namely, genetic or evolutionary learning. This paper will present a technique for the fusion of the three paradigms in a learning control context. It will describe a type of learning, known as Evolutionary Algorithm Reinforcement Learning (EARL), which is used to optimise a fuzzy neural control system. An application case study is also presented.