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We will examine stochastic weight update in the backpropagation algorithm on feed-forward neural networks. It was introduced by Salvetti and Wilamowski in 1994 in order to improve probability of convergence and speed of convergence. However, this update method has also one another quality, its implementation is simple for arbitrary network topology. In(More)
Stochastic weight update is a variant of error back-propagation algorithm for learning of artificial neural networks. It allows for efficient topology-independent implementation of backpropagation through time for recurrent networks. In stochastic weight update scenario, constant number of weights and neurons is randomly selected and updated. This is in(More)
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