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Adaptive exponential synchronization of delayed Cohen–Grossberg neural networks with discontinuous activations

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Abstract

This paper treats of the exponential synchronization issue of delayed Cohen–Grossberg neural networks with discontinuous activations. By utilizing Lyapunov stability theory, an adaptive controller is designed such that the response system can be exponentially synchronized with a drive system. Our synchronization criteria are easily verified and the obtained results are also applicable to neural networks with continuous activations since they are a special case of neural networks with discontinuous activations. Results of this paper improve a few previous known results. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.

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Authors and Affiliations

  1. Department of Applied Mathematics, Yanshan University, Qinhuangdao, 066001, China

    Huaiqin Wu, Xiaowei Zhang, Ruoxia Li & Rong Yao

Authors
  1. Huaiqin Wu
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  2. Xiaowei Zhang
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  3. Ruoxia Li
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  4. Rong Yao
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Corresponding author

Correspondence to Huaiqin Wu.

Additional information

This work was supported by the Natural Science Foundation of Hebei Province of China (A2011203103) and the Hebei Province Education Foundation of China (2009157).

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Wu, H., Zhang, X., Li, R. et al. Adaptive exponential synchronization of delayed Cohen–Grossberg neural networks with discontinuous activations. Int. J. Mach. Learn. & Cyber. 6, 253–263 (2015). https://doi.org/10.1007/s13042-014-0258-9

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  • DOI: https://doi.org/10.1007/s13042-014-0258-9

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