Neuro-Fuzzy Techniques under MATLAB/SIMULINK Applied to a Real Plant
نویسندگان
چکیده
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 this model under MATLAB/SIMULINK. This simulation environment supports the development of real time applications in an easy way.
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