Global asymptotic stability for RNNs with a bipolar activation function

Conditions for global asymptotic stability of a nonlinear relaxation process realized by a recurrent neural network with a hyperbolic tangent activation function are provided. This analysis is based upon the contraction mapping theorem and corresponding fixed point iteration. The derived results find their application in the wide area of neural networks for optimization and signal processing.