Title :
Neuro-fuzzy techniques under MATLAB/SIMULINK applied to a real plant
Author :
Nürnberger, Andreas ; Kruse, Rudolf
Author_Institution :
Fac. of Comput. Sci., Magdeburg Univ. of Technol., Germany
Abstract :
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 rule-base of a Mamdani-type fuzzy controller online by a reinforcement learning algorithm that uses a fuzzy error measure. An implementation of this model under MATLAB/SIMULINK is presented. This simulation environment supports the development of real time applications
Keywords :
control system CAD; fuzzy control; learning (artificial intelligence); neurocontrollers; optimisation; real-time systems; MATLAB; Mamdani-type; NEFCON model; SIMULINK; fuzzy control; fuzzy error; neural networks; neurofuzzy systems; optimization; reinforcement learning; Design optimization; Error correction; Fuzzy control; Fuzzy neural networks; Learning; MATLAB; Mathematical model; Neural networks; Process control; Process design;
Conference_Titel :
Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4863-X
DOI :
10.1109/FUZZY.1998.687549
