Abstract
AbstractBy utilizing the Lyapunov function method to analyze stability of discrete time Hopfield neural networks with delays and obtain some new sufficient conditions for the global exponential stability of the equilibrium point for such networks. It is shown that the proposed conditions rely on the connection matrices and network parameters. The presented conditions are testable and less conservative than some given in the earlier references.KeywordsNeural NetworkEquilibrium PointExponential StabilityRecurrent Neural NetworkCellular Neural NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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