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New Results for Exponential Synchronization of Memristive Cohen–Grossberg Neural Networks with Time-Varying Delays

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Abstract

This paper concerns the topic of exponential synchronization for a class of memristive Cohen–Grossberg neural networks with time-varying delays by designing a suitable controller. Through a nonlinear transformation, we obtain an alternative system from the considered memristive Cohen–Grossberg neural networks. Then, by studying the exponential synchronization of the alternative system, we get some novel and effective exponential synchronization criteria for the considered memristive Cohen–Grossberg neural networks. These results generalize some previous known results and remove a few restrictions on control width and time-delays. Finally, numerical simulations are given to present the effectiveness of the theoretical results.

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Acknowledgements

This work was supported by National Natural Science Foundation of Peoples Republic of China (Grants Nos. 61473244, 61563048, 11402223), Natural Science Foundation of Xinjiang (Grant No. 2014211B002).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou, 466001, China

    Mei Liu

  2. College of Mathematics and System Sciences, Xinjiang University, Ürümqi, 830046, Xinjiang, People’s Republic of China

    Mei Liu, Haijun Jiang & Cheng Hu

Authors
  1. Mei Liu
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  2. Haijun Jiang
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  3. Cheng Hu
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Correspondence to Mei Liu.

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Liu, M., Jiang, H. & Hu, C. New Results for Exponential Synchronization of Memristive Cohen–Grossberg Neural Networks with Time-Varying Delays. Neural Process Lett 49, 79–102 (2019). https://doi.org/10.1007/s11063-017-9728-x

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  • DOI: https://doi.org/10.1007/s11063-017-9728-x

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