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Resilient back propagation learning algorithm for recurrent fuzzy neural networks

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1 Author(s)
P. A. Mastorocostas ; Dept. of Informatics & Commun., Technol. & Inst. of Serres, Greece

An efficient training method for recurrent fuzzy neural networks is proposed. The method modifies the RPROP algorithm, originally developed for static neural networks, in order to be applied to dynamic systems. A comparative analysis with the standard back propagation through time is given, indicating the effectiveness of the proposed algorithm.

Published in:

Electronics Letters  (Volume:40 ,  Issue: 1 )