Neuro-fuzzy techniques under MATLAB/SIMULINK applied to a real plant

A. Nurnberger, R. Kruse
1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228)  
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 the application of an updated version of the neuro-fuzzy model NEFCON to a real plant. The NEFCON model is able to learn and optimize the rulebase of a Mamdani-type fuzzy controller online by a reinforcement learning algorithm that uses a fuzzy error measure. We used an implementation of
more » ... implementation of this model under MATLAB/SIMULINK. This simulation environment supports the development of real time applications in an easy way.
doi:10.1109/fuzzy.1998.687549 fatcat:andfl556hrcuripcc34y56nbii