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\({\varvec{p}}\)th Moment Exponential Stability of Hybrid Delayed Reaction–Diffusion Cohen–Grossberg Neural Networks

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Abstract

In this paper, we propose hybrid reaction–diffusion Cohen–Grossberg neural networks (RDCGNNs) with variable coefficients and mixed time delays. By using the Lyapunov–Krasovkii functional approach, stochastic analysis technique and Hardy inequality, some novel sufficient conditions are derived to ensure the pth moment exponential stability of hybrid RDCGNNs with mixed time delays. The obtained sufficient conditions are relevant to the diffusion terms. The results of this paper are novel and improve some of the previously known results. Finally, two numerical examples are provided to verify the usefulness of the obtained results.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China under Grants No. 60974139, China Postdoctoral Science Foundation Funded Project (2013M540754), Natural Science Foundation of Shaanxi Province under Grant No. 2015JM1015.

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Authors and Affiliations

  1. Institute of Nonlinear Science, Xianyang Normal University, Xianyang, 712000, People’s Republic of China

    Weiyuan Zhang

  2. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, People’s Republic of China

    Weiyuan Zhang, Junmin Li & Chenyang Ding

  3. The State Key Laboratory for Manufacturing Systems Engineering, and Systems Engineering Institute, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Weiyuan Zhang & Keyi Xing

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  1. Weiyuan Zhang
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  2. Junmin Li
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Correspondence to Weiyuan Zhang.

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Zhang, W., Li, J., Ding, C. et al. \({\varvec{p}}\)th Moment Exponential Stability of Hybrid Delayed Reaction–Diffusion Cohen–Grossberg Neural Networks. Neural Process Lett 46, 83–111 (2017). https://doi.org/10.1007/s11063-016-9572-4

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