Convergence analysis of stochastic pseudo-gradient algorithms and application to learning in feedforward neural networks

The convergence of a class of stochastic pseudo-gradient algorithms driven by correlated data sequences is considered in this paper. The obtained results are applied to a learning algorithm for feedforward neural networks and sufficient conditions for its convergence are determined.