Further Results on Mean-Square Exponential Input-to-State Stability of Stochastic Delayed Cohen-Grossberg Neural Networks

Wang, Wentao

doi:10.1007/s11063-022-10974-8

Cited by 11 publications

(2 citation statements)

References 28 publications

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“…As a dynamical behavior in CGNNs, stabilization plays an unique role in various fields such as engineering and science. For different aims, various types of stabilization were studied, such as asymptotic stabilization [16], exponential stabilization [17], finite-time stabilization [18], fixed-time stabilization [19], input-to-state stabilization [20], and H ∞ -stabilization [21]. Most of the authors previous focused on stabilization of CGNNs modeled by ordinary differential equations.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Gokulakrishnan

Sriniva

2023

Preprint

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The stabilization problem for a class of stochastic reaction-diffusion delayed Cohen-Grossberg neural networks (SRDDCGNNs) with event-triggered controller is addressed in this paper. To address such a problem, Neumann boundary condition, distributed and boundary external disturbances are introduced. New sufficient criteria are derived by using the Lyapunov method, event-triggered mechanism, and the linear matrix inequality (LMI) approach to ensure the proposed controlled systems achieve practically exponential input-to-state stabilization. In light of these criteria, the impact of an event-triggered controller on practically exponential input-to-state stability (PEISS) is examined. Furthermore , the obtained results are successfully applied to stochastic reaction-diffusion delayed cellular neural networks (SRDDCNNs) and stochastic reaction-diffusion delayed Hopfield neural networks (SRD-DHNNs). At last, simulation results are given to illustrate the main results, and the SRDDHNNs are applied to the image encryption.

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Section: Introductionmentioning

confidence: 99%

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Gokulakrishnan

Sriniva

2023

Preprint

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“…Remark To the best of our knowledge, except [30, 31], there have been few results on the input‐to‐state stability of stochastic neural networks under local Lipschitz condition. As we all know that there exist functions like

x-{x}&#x0005E;3,\sin {x}&#x0005E;2

and many other nonlinear ones that do not satisfy uniform Lipschitz condition, but local Lipschitz condition.…”

Section: Introductionmentioning

confidence: 99%

Mean‐square exponential input‐to‐state stability of stochastic delayed recurrent neural networks with local Lipschitz condition

Wang

2023

Math Methods in App Sciences

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We consider the global existence of solutions and input‐to‐state stability for a class of stochastic delayed recurrent neural networks without uniform Lipschitz condition. Under local Lipschitz condition, we find new sufficient conditions that ensure the solutions of given neural networks exist globally and are mean‐square exponentially input‐to‐state stable. Furthermore, we highlight the advantages of our novel results by comparing with some known results as well as a numerical example.

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EyeDeep-Net: a multi-class diagnosis of retinal diseases using deep neural network

Sengar

Joshi

Dutta

et al. 2023

Neural Comput & Applic

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Further Results on Mean-Square Exponential Input-to-State Stability of Stochastic Delayed Cohen-Grossberg Neural Networks

Cited by 11 publications

References 28 publications

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Mean‐square exponential input‐to‐state stability of stochastic delayed recurrent neural networks with local Lipschitz condition

EyeDeep-Net: a multi-class diagnosis of retinal diseases using deep neural network

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