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- W2976887353 abstract "This paper mainly discusses the input-to-state stability for BAM neural networks with time-varying delays and Markov jump parameters. Considering the system with Markov jump parameters, we select the improved criterion, namely mean-square exponential input-to-state stability. With the help of stochastic theory, we establish the Markovian switched Lyapunov–Krasovskill functional and obtain its derivation by making use of the weak infinitesimal operator, which is used to obtain the algebraic and linear matrix inequality(LMI) conditions. These conditions can ensure that the system is mean-square exponentially input-to-state stable. In particular, we design a controller to simplify the algebraic conditions. Finally, we provide two numerical examples to show the effectiveness and superiority of the obtained results." @default.
- W2976887353 created "2019-10-03" @default.
- W2976887353 creator A5023725857 @default.
- W2976887353 creator A5067339383 @default.
- W2976887353 date "2020-02-01" @default.
- W2976887353 modified "2023-10-13" @default.
- W2976887353 title "Further results on mean-square exponential input-to-state stability of time-varying delayed BAM neural networks with Markovian switching" @default.
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- W2976887353 doi "https://doi.org/10.1016/j.neucom.2019.09.033" @default.
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