Convergence of an online gradient method for feedforward neural networks with stochastic inputs

摘要

In this paper, we study the convergence of an online gradient method for feed-forward neural networks. The input training examples are permuted stochastically in each cycle of iteration. A monotonicity and a weak convergence of deterministic nature are proved.