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New conditions for global exponential stability of continuous-time neural networks with delays

  • Cont. Dev. of Neural Compt. & Appln.
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Abstract

In this paper, we investigate the global exponential stability of delayed neural network systems. For this purpose, the activation functions are assumed to be globally Lipschitz continuous. The properties of norms and the relationship of homeomorphism are adjusted to ensure the existence as well as the uniqueness of the equilibrium point. Then by employing suitable Lyapunov functional, some delay-independent sufficient conditions are derived for exponential convergence toward global equilibrium state associated with different input sources. The obtained results are shown to be more general and less restrictive than the previous results derived in the literature. Lastly, a number of examples are provided to demonstrate the validity of the results proposed.

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Authors and Affiliations

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China

    Haibo Gao, Xingguo Song, Liang Ding & Minghui Hao

  2. Yanshan University, Qinhuangdao, 066004, China

    Deyou Liu

Authors
  1. Haibo Gao
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  2. Xingguo Song
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  3. Liang Ding
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  4. Deyou Liu
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  5. Minghui Hao
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Corresponding author

Correspondence to Xingguo Song.

Additional information

This work was supported by the National Natural Science Foundation of China (50975059/61005080), China Postdoctoral Special Science Foundation (201104405), China Postdoctoral Science Foundation (20100480994), and the “111” Project (B07018).

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Gao, H., Song, X., Ding, L. et al. New conditions for global exponential stability of continuous-time neural networks with delays. Neural Comput & Applic 22, 41–48 (2013). https://doi.org/10.1007/s00521-011-0745-9

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  • DOI: https://doi.org/10.1007/s00521-011-0745-9

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