A simulated annealing-based learning algorithm for block-diagonal recurrent neural networks
Paris Mastorocostas, Dimitrios Varsamis, C. Mastorocostas · International conference on Artificial intelligence and applications · 2006
A fast and efficient training method for block-diagonal recurrent fuzzy neural networks is proposed. The method modifies the Simulated Annealing RPROP algorithm, originally developed for static models, in order to be applied to dynamic systems. A comparative analysis with a series of algorithms and recurrent models is given, indicating the effectiveness of the proposed learning approach.