A Neuro-Fuzzy Development Tool for Fuzzy Controllers under MATLAB/SIMULINK

: The design and optimization process of fuzzy controllers can be supported by learning techniques derived from neural networks. Such approaches are usually called neuro-fuzzy systems. In this paper we describe an updated version of the neuro-fuzzy model NEFCON. This model is able to learn and optimize the rulebase 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 of a dynamic system. In addition we present an implementation of the model and an application example under the MATLAB/SIMULINK development environment. The implementation uses a graphical user interface to control the learning process interactively. The optimized fuzzy controller can be detached from the development environment and can be used in real time environments. The tool is available free of charge for non-commercial purposes (http://fuzzy.cs.uni-magdeburg.de/nefcon). 1 INTRODUCTIO..