Title :
Recurrent fuzzy systems
Author :
Gorrini, V. ; Bersini, Hugues
Author_Institution :
IRIDIA, Univ. Libre de Bruxelles, Belgium
Abstract :
Besides their linguistic interface, we believe fuzzy controllers not only to be universal approximators but also more general and efficient than their similar neural counterparts: radial basis functions. Consequently like recurrent neural networks, this paper aims at extending the fuzzy controllers approximation capacity to dynamic processes of unknown order. We propose a new type of architecture called recurrent fuzzy system together with a learning algorithm for adapting the membership functions
Keywords :
fuzzy control; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); parallel architectures; recurrent neural nets; approximation capacity; architecture; dynamic processes; fuzzy controllers; learning algorithm; membership functions; recurrent fuzzy systems; recurrent neural networks; Automatic control; Control systems; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Input variables; Multi-layer neural network; Neural networks; Nonlinear control systems; Process control;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343687
