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Global asymptotic stability of discrete-time recurrent neural networks

This paper presents new analytical results on the global asymptotic stability for the equilibrium states of a general class of discrete-time recurrent neural networks (DTRNNs) described by using a set of nonlinear difference equations. We provide a few sufficient conditions for the global asymptotic stability of DTRNNs. The resulting criteria include diagonal stability and nondiagonal stability. These stability conditions are less restrictive than the existing ones in the literature.