A new convergence property of online BP learning

Abstract

The feedforward neural networks trained with the online backpropagation (BP) learning algorithm have been widely studied in various areas of scientific research and engineering applications. In this paper we further study the convergence property of the online BP learning algorithm. Unlike the existing convergence analysis mainly focusing on the convergence of the gradient sequence of the error functions, we prove a convergence theorem for the sequence of the error functions itself.