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Global Exponential Convergence of HCNNs with Neutral Type Proportional Delays and D Operator

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Abstract

This paper deals with a class of high-order cellular neural networks with neutral type proportional delays and D operator. By applying differential inequality techniques, we show that all solutions of the addressed system converge exponentially to zero vector. In addition, we provide an example and its numerical simulations to demonstrate the effectiveness of the proposed results.

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Acknowledgements

The author would like to express the gratitude to the editors and anonymous reviewers for their valuable suggestions, which improved the presentation of this paper. This work was supported by the Natural Scientific Research Fund of Zhejiang Province of China (Grant No. LY18A010019), a Key Project Supported by Scientific Research Fund of Hunan Provincial Education Department (15A038) and Natural Scientific Research Fund of Hunan Provincial of China (Grant Nos. 2016JJ6103, 2016JJ6104).

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Authors and Affiliations

  1. Academy of Intelligent Software, Guangzhou University, Guangdong Provincial Engineering Technology Research Center for Mathematical Educational Software, Guangzhou, 510006, Guangdong, People’s Republic of China

    Songlin Xiao

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  1. Songlin Xiao
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Correspondence to Songlin Xiao.

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Xiao, S. Global Exponential Convergence of HCNNs with Neutral Type Proportional Delays and D Operator. Neural Process Lett 49, 347–356 (2019). https://doi.org/10.1007/s11063-018-9817-5

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