Learning in neural networks and stochastic approximation methods with averagingShcherbakov, P. S., Tikhonov, S. N., Warwick, K. and Mason, J. D. (1994) Learning in neural networks and stochastic approximation methods with averaging. In: IEE Colloquium on Advances in Neural Networks for Control and Systems, 25-27 May 1994, Berlin, Germany, 14/1-14/4. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Abstract/SummaryThe problem of adjusting the weights (learning) in multilayer feedforward neural networks (NN) is known to be of a high importance when utilizing NN techniques in various practical applications. The learning procedure is to be performed as fast as possible and in a simple computational fashion, the two requirements which are usually not satisfied practically by the methods developed so far. Moreover, the presence of random inaccuracies are usually not taken into account. In view of these three issues, an alternative stochastic approximation approach discussed in the paper, seems to be very promising.
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