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Global synchronization of memristive hybrid neural networks via nonlinear coupling

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Abstract

This paper probes into the synchronization for memristor-based hybrid neural networks via nonlinear coupling. At first, a new condition is established to judge whether quadratic functions are negative or not on a closed interval regardless of their concavity or convexity. Then, by utilizing Legendre orthogonal polynomials, a recent extended integral inequality with free matrices is popularized to get tighter lower bound of some integral terms. Next, based on a novel Lyapunov functional, by applying our new integral inequality with free matrices, linear convex combination method and the new criterion, a new delay-dependent condition is gained to reach the global synchronization for the considered neural networks. At last, an example is presented to account for the validity of our results.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61273022, 61433004, 61627809) and the Research Foundation of Department of Education of Liaoning Province (No. JDL2017031).

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Authors and Affiliations

  1. Department of Mathematics, Dalian Jiaotong University, Dalian, 116028, China

    Cheng-De Zheng & Lulu Zhang

  2. School of Information Science and Engineering, Northeastern University, 110004, Shenyang, China

    Huaguang Zhang

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  1. Cheng-De Zheng
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  2. Lulu Zhang
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  3. Huaguang Zhang
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Correspondence to Cheng-De Zheng.

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Zheng, CD., Zhang, L. & Zhang, H. Global synchronization of memristive hybrid neural networks via nonlinear coupling. Neural Comput & Applic 33, 2873–2887 (2021). https://doi.org/10.1007/s00521-020-05166-1

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