Stochastic Approximation and Multilayer Perceptrons: The Gain Backpropagation Algorithm

A standard general algorithm, the stochastic approximation algorithm of Albert and Gardner [1] , is applied in a new context to compute the weights of a multilayer per ceptron network. This leads to a new algorithm, the gain backpropagation algorithm, which is related to, but significantly different from, the standard backpropagat ion algorith m [2]. Some simulation examples show the potential and limitations of the proposed approach and provide comparisons with the conventional backpropagation algorithm.