No abstract

This paper is concerned with delay-dependent passivity analysis for delayed neural networks (DNNs) of neutral type. We first discuss the passivity conditions for DNNs without uncertainties and then extend this result to the case of interval uncertainties. By partitioning the delay intervals into multiple equidistant subintervals, some appropriate Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals. Considering these new LKFs and using free-weighting matrix approach, several new passivity criteria are proposed in terms of linear matrix inequalities, which are dependent on the size of the time delay. Finally, five numerical examples are given to illustrate the effectiveness and less conservatism of the developed techniques.

No abstract

SummaryIn this paper, the problem of dissipativity and passivity analysis is investigated for discrete‐time complex‐valued neural networks with time‐varying delays. Both leakage and discrete time‐varying delays have been considered. By constructing a suitable Lyapunov–Krasovskii functional and by using discretized Jensen's inequality approach, sufficient conditions have been established to guarantee the (Q,S,R) − γ dissipativity and passivity of the addressed discrete‐time complex‐valued neural networks. These conditions are derived in terms of complex‐valued linear matrix inequalities (LMIs), which can be checked numerically using Yet Another LMI Parser toolbox in Matrix Laboratory. Finally, three numerical examples are established to illustrate the effectiveness of the obtained theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

No abstract

No abstract

No abstract

In this article, we consider the problem of robust dissipativity and passivity analysis for a class of general discretetime recurrent neural networks (NNs) with time-varying delays. The NN under consideration is subject to timevarying and norm bounded parameter uncertainties. By the latest free-weighting matrix method, an appropriate Lyapunov-Krasovskii functional and using stochastic analysis technique a sufficient condition is established to ensure that the NNs under consideration is strictly ðQ; S; RÞ-dissipative. The derived conditions are presented in terms of linear matrix inequalities. Numerical examples and its simulations are given to demonstrate the effectiveness of the results. V C 2014 Wiley Periodicals, Inc. Complexity 21: [47][48][49][50][51][52][53][54][55][56][57][58] 2016