Exponential convergence of delayed cellular neural networks with time-varying coefficients (Q953894)

The paper investigates the convergence behavior of a cellular neural network with time-varying delays and continuously distributed delays. In the mathematical model of this class of cellular neural network, it is assumed that the neuron activation functions don't satisfy a Lipschitz condition, the time-varying delays are bounded, and the coefficients are time-varying. By applying some mathematical analytic techniques, they derive some new sufficient conditions ensuring that all solutions of the cellular neural network system converge exponentially to the equilibrium point. An example is carried out to illustrate the main results.