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New Results on Convergence of CNNs with Neutral Type Proportional Delays and D Operator

  • Guangyi Yang
  • Weipin Wang
Article

Abstract

Based on differential inequality technique, we show that all solutions of a class of cellular neural networks with neutral type proportional delays and D operator converge exponentially to zero vector. In particular, we obtain the convergence rate estimation for global exponential stability of the addressed system. We also give two simulations examples to verify our theoretical findings.

Keywords

Global exponential convergence Cellular neural network D operator Neutral type proportional delay 

Mathematics Subject Classification

92C42 93D20 94D05 65L20 

Notes

Acknowledgements

The authors would like to thank the anonymous referees and the editor for very helpful suggestions and comments which led to improvements of our original paper. This work was supported by Natural Scientific Research Fund of Hunan Provincial of China (Grant Nos. 2018JJ2372, 2018JJ2087), and Natural Scientific Research Fund of Hunan Provincial Education Department of China (Grant No. 17C1076).

Compliance with Ethical Standards

Conflict of interest

Authors declare that they have no conflict of interest.

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Copyright information

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

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaPeople’s Republic of China
  2. 2.Hunan Institute of Metrology and TestChangshaPeople’s Republic of China

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