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Global Exponential Stability of Hybrid Non-autonomous Neural Networks with Markovian Switching

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Abstract

This paper discusses the global exponential stability for a class of hybrid non-autonomous neural networks (HNNNs) with Markovian switching, which includes the factors of time delays and impulse disturbance. A novel Halanay inequality with cross terms is established by using stochastic analysis technique. Some sufficiency criteria for the global exponential stability of the HNNNs with Markovian switching are derived by the Halanay inequality and some mathematical analysis methods. The results obtained have better fault tolerance and redundancy under certain accuracy than the existing results in the literature. Finally, numerical experiments are provided to illustrate our theoretical results.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Key Program) under Grant 61836010. The authors would like to thank their laboratory team member’s assistance.

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Authors and Affiliations

  1. Department of Electronic Engineering, School of Electronic Science and Engineering, Xiamen University, Xiamen, 361005, China

    Chenhui Zhao & Donghui Guo

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  1. Chenhui Zhao
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  2. Donghui Guo
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Correspondence to Chenhui Zhao.

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Zhao, C., Guo, D. Global Exponential Stability of Hybrid Non-autonomous Neural Networks with Markovian Switching. Neural Process Lett 52, 525–543 (2020). https://doi.org/10.1007/s11063-020-10262-3

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