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

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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.

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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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Authors and Affiliations

  1. Department of Electronic and Information Engineering, Bozhou University, Bozhou, 236800, China

    Dong Xie

  2. College of Mathematics and Econometrics, Hunan University, Changsha, 410082, China

    Dong Xie, Yueping Jiang & Minghua Han

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  1. Dong Xie
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  2. Yueping Jiang
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  3. Minghua Han
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Correspondence to Dong Xie.

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Xie, D., Jiang, Y. & Han, M. Global Exponential Synchronization of Complex-Valued Neural Networks with Time Delays via Matrix Measure Method. Neural Process Lett 49, 187–201 (2019). https://doi.org/10.1007/s11063-018-9805-9

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