The paper shows that it fully connected neural networks are used then the same problem can be solved with less number of neurons and weights. Interestingly such networks are trained faster. The problem is that most of the neural networks terming algorithms are not suitable for such network. Presented algorithm and software allow training feedforwad neural… (More)

Introducing Stochastic Process within the Backpropagation Algorithm for Improved Convergence”, presented at ANNIE'94 - Artificial Neural Networks

A. Salvetti, B. M. Wilamowski

1994

1 Excerpt

Learning representations by back-propagating errors

D. E. Rumenhart, G. E. Hinton, Wiliams, R. J

Nature, vol

1986

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@article{Wilamowski2007MethodOC,
title={Method of computing gradient vector and Jacobean matrix in arbitrarily connected neural networks},
author={B. M. Wilamowski and N J Cotton and Ovg{\"u} Kaynak and Gokce Dundar},
journal={2007 IEEE International Symposium on Industrial Electronics},
year={2007},
pages={3298-3303}
}