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Global Exponential Synchronization of Delayed Complex-Valued Recurrent Neural Networks with Discontinuous Activations

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Abstract

In this paper, we are concerned with the exponential synchronization for a class of two delayed complex-valued recurrent neural networks (CVRNNs) with discontinuous neuron activations. By separating CVRNNs into real and imaginary parts, forming an equivalent real-valued subsystems, under the framework of differential inclusions, novel state feedback controllers are designed and novel criteria are established to ensure the exponential stability of error system, and thus the drive system exponentially synchronize with the response system. The obtained results are essentially new and complement previously known ones. The practicability of theoretical results is also supported via a numerical example.

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Authors and Affiliations

  1. School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, 232001, Anhui, People’s Republic of China

    Lian Duan & Min Shi

  2. School of Mathematics and Statistics, Central South University, Changsha, 410083, Hunan, People’s Republic of China

    Lian Duan

  3. Department of Mathematics, Hunan First Normal University, Changsha, 410205, Hunan, People’s Republic of China

    Zengyun Wang

  4. School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, 410114, Hunan, People’s Republic of China

    Lihong Huang

Authors
  1. Lian Duan
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  2. Min Shi
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  3. Zengyun Wang
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  4. Lihong Huang
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Correspondence to Lian Duan.

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This work was jointly supported by the National Natural Science Foundation of China (11701007, 11601143, 11771059), Natural Science Foundation of Anhui Province (1808085QA01), Key Program of University Natural Science Research Fund of Anhui Province (KJ2017A088, KJ2018A0082), China Postdoctoral Science Foundation (2018M640579), Key Program of Scientific Research Fund for Young Teachers of AUST (QN201605), Open Fund of Hunan Provincial Key Laboratory of Engineering Mathematics Modeling and Analysis (2018MMAEZD17), and the Doctoral Fund of AUST (11668).

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Duan, L., Shi, M., Wang, Z. et al. Global Exponential Synchronization of Delayed Complex-Valued Recurrent Neural Networks with Discontinuous Activations. Neural Process Lett 50, 2183–2200 (2019). https://doi.org/10.1007/s11063-018-09970-8

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