Analysis of learning recurrent neural networks: connective stability and equilibrium manifold

Abstract

Stability analysis of recurrent neural networks with a learning rule based on the concept of an equilibrium manifold is considered. Recurrent neural networks with learning rules have changing equilibria during the learning process. The authors design a learning rule that enables the recurrent neural network to store a desired pattern based on the concept of the equilibrium manifold. A stability criterion for the learning neural network is established and is a function of the learning rate, a sigmoid function and the upper bound of the interconnection strength. >