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Mean Square Exponential Stability of Neutral Stochastic Delay Neural Networks

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Advances in Neural Networks – ISNN 2024 (ISNN 2024)

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Abstract

This paper investigates the mean square exponential stability of neutral stochastic delay neural networks through the utilization of a novel methodology. New explicit equations are derived to analyze the mean square exponential stability of the system. Furthermore, the effectiveness of the proposed approach is demonstrated through the illustrative example.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61873271, 62006233; Research Funds for the Central Universities 2018XKQYMS15 and the Double-First-Rate Special Fund for Construction of China University of Mining and Technology No.2018ZZCX14.

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Authors and Affiliations

  1. School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China

    Han Yu & Song Zhu

  2. Jiangsu Center for Applied Mathematics (CUMT), Xuzhou, 221116, China

    Han Yu & Song Zhu

Authors
  1. Han Yu
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  2. Song Zhu
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Corresponding author

Correspondence to Song Zhu .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Xinyi Le

  2. South China University of Technology, Guangzhou, China

    Zhijun Zhang

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Yu, H., Zhu, S. (2024). Mean Square Exponential Stability of Neutral Stochastic Delay Neural Networks. In: Le, X., Zhang, Z. (eds) Advances in Neural Networks – ISNN 2024. ISNN 2024. Lecture Notes in Computer Science, vol 14827. Springer, Singapore. https://doi.org/10.1007/978-981-97-4399-5_33

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  • DOI: https://doi.org/10.1007/978-981-97-4399-5_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-4398-8

  • Online ISBN: 978-981-97-4399-5

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