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Neural Processing Letters

, Volume 49, Issue 1, pp 187–201 | Cite as

Global Exponential Synchronization of Complex-Valued Neural Networks with Time Delays via Matrix Measure Method

  • Dong XieEmail author
  • Yueping Jiang
  • Minghua Han
Article

Abstract

In this paper, global exponential synchronization of a class of complex-valued neural networks with time delays is investigated. Based on Halanay inequality theory, Lyapunov theory and matrix measure method, by separating complex-valued neural networks to the real part and imaginary part, several criteria for the global exponentially synchronization of complex-valued neural networks are presented. Finally, one numerical simulation is given to show the effectiveness of our theoretical results.

Keywords

Complex-valued neural networks Global exponential synchronization Matrix measure Halanay inequality Time delays 

Notes

Acknowledgements

The work was jointly supported by the National Natural Science Foundation of China under Grant No. 11371126, the High School Outstanding Young Support Plan of Anhui Province under Grant No. gxyq2014175, the Natural Science Research Project of Anhui Province under Grant Nos. KJ2015A347, KJ2017A704, and the Key Project of Natural Science Research of Bozhou University under Grant No. BYZ2017B03.

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Copyright information

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

Authors and Affiliations

  1. 1.Department of Electronic and Information EngineeringBozhou UniversityBozhouChina
  2. 2.College of Mathematics and EconometricsHunan UniversityChangshaChina

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