Dynamic programming approach to optimal weight selection in multilayer neural networks

A novel algorithm for weight adjustments in a multilayer neural network is derived using the principles of dynamic programming. The algorithm computes the optimal values for weights on a layer-by-layer basis starting from the output layer of the network. The advantage of this algorithm is that it provides an error function for every hidden layer expressed entirely in terms of the weights and outputs of the hidden layer, and minimization of this error function yields the optimum weights for the hidden layer.