Title :
Resilient back propagation learning algorithm for recurrent fuzzy neural networks
Author :
Mastorocostas, P.A.
Author_Institution :
Dept. of Informatics & Commun., Technol. & Educ.al Inst. of Serres, Greece
Abstract :
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.
Keywords :
backpropagation; fuzzy neural nets; mean square error methods; recurrent neural nets; recursive functions; adaptation mechanism; efficient training method; first-order learning methods; fitting parameter; mean squared error; modified RPROP algorithm; ordered partial derivatives; recurrent fuzzy neural networks; recursive equations; resilient backpropagation learning algorithm;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:20040052
