Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications
2013
Session Number:C1L-D
Session:
Number:386
Back Propagation Learning of Neural Networks with Replicated Neurons
Naohiro Shibuya, Yuta Yokoyama, Chihiro Ikuta, Yoko Uwate, Yoshifumi Nishio,
pp.386-389
Publication Date:
Online ISSN:2188-5079
[1] T. Poggio and F. Girosi, “Networks for approximation and learning”, Proc. IEEE, vol.78, no.9, pp.1481-1497, 1990.
[2] K. Hornik , M. Stinchcombe and H. White, “Multilayer feedforward networks are universal approximators”, Neural Networks, vol.2, pp.359-366, 1989.
[3] Y. Uwate and Y. Nishio, “Durability of Affordable Neural Networks against Damages”, International Joint Conference on Neural Networks (IJCNN'06), pp.8365-8370, July, 2006.
[4] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning representations by back-propagating errors”, Nature, vol.323-9, pp.533-536, 1986.
[5] Takeshi Agui, Hiroshi Nagahashi, Hiroki Takahashi, “Neural Program”, Shokoudou corp, pp.11-18, 20-42, 1995.
[6] Jain. Rarnesh C, “A robust backpropagation learning algorithm for function approximation”, Journals, vol.5, pp.467-479, May, 1994.
[7] Horng-Lin Shieh, Ying-Kuei Yang, Po-Lun Chang, Jin-Tsong Jeng, “Robust neural-fuzzy method for function approximation”, Expert Systems with Applications, vol.36, pp.6903-6913, April, 2009.