Deterministic convergence of an online gradient method for neural networks

Abstract

The online gradient method has been widely used as a learning algorithm for neural networks. We establish a deterministic convergence of online gradient methods for the training of a class of nonlinear feedforward neural networks when the training examples are linearly independent. We choose the learning rate η to be a constant during the training procedure. The monotonicity of the error function in the iteration is proved. A criterion for choosing the learning rate η is also provided to guarantee the convergence. Under certain conditions similar to those for the classical gradient methods, an optimal convergence rate for our online gradient methods is proved.