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Issue:Modelling the backpropagation algorithm of the Elman neural network by a generalized net

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Title of paper: Modelling the backpropagation algorithm of the Elman neural network by a generalized net
Author(s):
Sotir Sotirov
“Prof. Asen Zlatarov” University, 1 “Prof. Yakimov” Blvd, Burgas–8010, Bulgaria
ssotirov@btu.bg
Presented at: 13th IWGN, London, 29 October 2012
Published in: Conference proceedings, pages 49—55
Download: Download-icon.png PDF (136  Kb, Info)
Abstract: The proposed GN model presents the functioning of recurrent neural networks. Here we discuss the Elman network and the ‘backpropagation’ algorithm for learning. In comparison with other types of neural networks, here we describe the process in its temporal development. In a series of papers, we have described many different neural networks using the apparatus of generalized nets. The present research deals with another kind – neural network with feedback into the hidden layer.
Keywords: Neural networks, Recurrent neural networks, Elman neural network, Generalized net, Backpropagation algorithm.
AMS Classification: 68Q85, 62M45.
References:
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  6. Rumelhart, D., G. Hinton, R. Williams. Training representation by back-propagation errors, Nature, Vol. 323, 1986, 533–536.
  7. Sotirov, S. Modeling the algorithm Backpropagation for training of neural networks with generalized nets. Part 1, Proc. of the Fourth International Workshop on Generalized Nets, Sofia, 23 September 2003, 61–67.
  8. Sotirov, S., E. El-Darzi, Generalized net model of the Elman neural network, Proc. of the Eleventh Int. Workshop on GNs and Second Int. Workshop on GNs, IFSs, KE, London, 9–10 July 2010, 21–26.
  9. Haykin, S., Neural Networks and Learning Machines, McMaster University, Canada, 2008.
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