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

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Abstract

We consider the global existence of solutions and mean-square exponential input-to-state stability for a class of stochastic delayed Cohen-Grossberg neural networks without global 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 the results in Zhou et al. (2015) as well as a numerical example.

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Acknowledgements

This work was supported by the Natural Scientific Research Fund of Zhejiang Provincial of China (grant no. LY18A010019, LY16A010018).

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Correspondence to Wentao Wang.

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Wang, W. Further Results on Mean-Square Exponential Input-to-State Stability of Stochastic Delayed Cohen-Grossberg Neural Networks. Neural Process Lett 55, 3953–3965 (2023). https://doi.org/10.1007/s11063-022-10974-8

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