A new error function at hidden layers for past training of multilayer perceptrons

This paper proposes a new error function at hidden layers to speed up the training of multilayer perceptrons (MLPs). With this new hidden error function, the layer-by-layer (LBL) algorithm approximately converges to the error backpropagation algorithm with optimum learning rates. Especially, the optimum learning rate for a hidden weight vector appears approximately as a multiplication of two optimum factors, one for minimizing the new hidden error function and the other for assigning hidden targets. Effectiveness of the proposed error function was demonstrated for handwritten digit recognition and isolated-word recognition tasks. Very fast learning convergence was obtained for MLPs without the stalling problem experienced in conventional LBL algorithms