A backpropagation learning framework for feedforward neural networks

Abstract

In this paper, a general backpropagation learning framework for the training of feedforward neural networks is proposed. The convergence to global minimum under the framework is investigated using the Lyapunov stability theory. It is shown the existing feedforward neural network training algorithms are special cases of the proposed framework.