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• W2775707325 abstract "In this paper, we concentrate on the problem of global asymptotical stability for a class of Markovian jump inertial Cohen–Grossberg neural networks. The jumping parameters are described with a continuous-time, finite-state Markov chain. By adopting the method of model transformation, differential mean value theorem, Lyapunov stability theory and linear matrix inequality techniques, we derive some novel sufficient conditions to guarantee the global asymptotical stability for the addressed systems. It is worth mentioning that the model investigated in this letter comprises and generalizes many existing results in the previous literature. Finally, the effectiveness of the theoretical results is validated by numerical examples." @default.
• W2775707325 created "2017-12-22" @default.
• W2775707325 creator A5017808266 @default.
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• W2775707325 date "2018-03-01" @default.
• W2775707325 modified "2023-10-09" @default.
• W2775707325 title "Stability analysis of inertial Cohen–Grossberg neural networks with Markovian jumping parameters" @default.
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• W2775707325 doi "https://doi.org/10.1016/j.neucom.2017.12.028" @default.
• W2775707325 hasPublicationYear "2018" @default.
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