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Neural Processing Letters

, Volume 49, Issue 1, pp 67–78 | Cite as

Exponential Stability of Positive Recurrent Neural Networks with Multi-proportional Delays

  • Gang YangEmail author
Article

Abstract

This paper presents some new results on the existence, uniqueness and generalized exponential stability of a positive equilibrium for positive recurrent neural networks with multi-proportional delays. Based on the differential inequality techniques, a testable condition is established to guarantee that all solutions of the considered system converge exponentially to a unique positive equilibrium. The effectiveness of the obtained results is illustrated by a numerical example.

Keywords

Positive recurrent neural network Generalized exponential stability Proportional delay 

Mathematics Subject Classification

34C25 34K13 34K25 

Notes

Acknowledgements

The author would like to express the sincere appreciation to the editor and reviewers for their helpful comments in improving the presentation and quality of the paper. In particular, the author expresses the sincere gratitude to Prof. Jianying Shao (Jiaxing University, Zhejiang) for the helpful discussion when this revision work was being carried out. This work was supported by National Social Science Fund of China (Grant No. 15BJY122), Natural Scientific Research Fund of Hunan Provincial of China (Grant Nos. 2016JJ6103, 2016JJ6104), Zhejiang Provincial Natural Science Foundation of China (Grant No. LY18A010019), and Natural Scientific Research Fund of Hunan Provincial Education Department of China (Grant No. 17C1076).

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Copyright information

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

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsHunan University of CommerceChangshaPeople’s Republic of China

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