Neuro-fuzzy control based on the NEFCON-model: recent developments

Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. One popular approach is to combine fuzzy systems with learning techniques derived from neural networks. Such approaches are usually called neuro-fuzzy systems. In this paper we present our view of neuro-fuzzy systems and an implementation in the area of control theory: the NEFCON-Model. This model is able to learn and optimize the rule base of a Mamdani like fuzzy controller online by a reinforcement learning algorithm that uses a fuzzy error measure. Therefore, we also describe some methods to determine a fuzzy error measure for a dynamic system. In addition we present some implementations of the model and an application example. The presented implementations are available free of charge for non-commercial purposes.