期刊
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
卷 30, 期 16, 页码 6535-6568出版社
WILEY
DOI: 10.1002/rnc.5118
关键词
additive time-varying delay; exponential stability; extended dissipativity; Hardy-Poincare inequality; memristive neural networks; quadratically convex combination approach
资金
- Jiangsu Provincial Key Laboratory of Networked Collective Intelligence [BM2017002]
- National Natural Science Foundation of China [11601474]
This article deals with the problems of exponential stability and extended dissipative analysis for a class of uncertain memristive neural networks (MNNs) with additive time-varying delays and reaction-diffusion terms. On the basis of generalized Lyapunov functional approach, Hardy-Poincare inequality, Jensen's inequality, as well as some other inequality techniques, it is shown that the issues of exponential stability and extended dissipativity for the uncertain reaction-diffusion MNNs are solvable if a set of linear matrix inequalities (LMIs) proposed are feasible. As a special case, the conditions on exponential stability and extended dissipativity for the uncertain MNNs with additive time-varying delays are also obtained and given in terms of nonlinear matrix inequalities (NMIs). The obtained NMIs can be transformed as general LMIs via using a new quadratic convex combination skill. Moreover, the developed sufficient conditions for the uncertain delayed MNNs with and without reaction-diffusion terms can be easily checked by Matlab LMI control toolbox, and three numerical examples with simulation are provided to show the effectiveness and applicability of the theoretical results.
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