This paper considers the problem of robust delay- dependent Passivity analysis for time-varying delayed neural networks described by nonlinear delay differential equations of the neutral type, which is subject to norm-bounded time-varying parameter uncertainties. The activation functions are supposed to be bounded and globally Lipschitz continuous. Both delay-dependent and delay-independent passivity conditions are proposed by using more general Lyapunov-Krasovskii functionals. These passivity conditions are obtained in terms of linear matrix inequalities, which can be investigated easily by recently using standard algorithms. An illustrative example is provided to demonstrate the effectiveness and the reduced conservatism of the proposed method. Index Term passivity, linear matrix inequality, neural networks, uncertainty, delay dependence.
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