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Get Free AccessIn this paper, we analyze the exponential stability, passivity, and (Q, G, R)-γ-dissipativity of generalized neural networks (GNNs) including mixed time-varying delays in state vectors. Novel exponential stability, passivity, and (Q, G, R)-γ-dissipativity criteria are developed in the form of linear matrix inequalities for continuous-time GNNs by constructing an appropriate Lyapunov-Krasovskii functional (LKF) and applying a new weighted integral inequality for handling integral terms in the time derivative of the established LKF for both single and double integrals. Some special cases are also discussed. The superiority of employing the method presented in this paper over some existing methods is verified by numerical examples.
Ramasamy Saravanakumar, Grienggrai Rajchakit, Choon Ki Ahn, Hamid Reza Karimi (2017). Exponential Stability, Passivity, and Dissipativity Analysis of Generalized Neural Networks With Mixed Time-Varying Delays. IEEE Transactions on Systems Man and Cybernetics Systems, 49(2), pp. 395-405, DOI: 10.1109/tsmc.2017.2719899.
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Type
Article
Year
2017
Authors
4
Datasets
0
Total Files
0
Language
English
Journal
IEEE Transactions on Systems Man and Cybernetics Systems
DOI
10.1109/tsmc.2017.2719899
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