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Neural Processing Letters

, Volume 49, Issue 1, pp 347–356 | Cite as

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

  • Songlin XiaoEmail author
Article

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.

Keywords

Global exponential convergence High-order cellular neural network D operator Neutral type proportional delay 

Mathematics Subject Classification

34C25 34K13 34K25 

Notes

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).

Compliance with Ethical Standards

Conflicts of interest

The Author declares that he have no conflict of interests.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Academy of Intelligent SoftwareGuangzhou University, Guangdong Provincial Engineering Technology Research Center for Mathematical Educational SoftwareGuangzhouPeople’s Republic of China

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