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Novel Switching Jumps Dependent Exponential Synchronization Criteria for Memristor-Based Neural Networks

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Abstract

This paper investigates the problem of global exponential synchronization for memristor-based neural networks with delay. Based on the nonsmooth analysis and differential inclusion theory, a new analytic technique is employed to design a discontinuous state feedback controller, which ensures the memristor-based drive system exponential synchronize with the response system. The succinct synchronization conditions is closely relate to the switching jumps. The estimated rate of the exponential synchronization can be obtained by solving a sample algebra equation. Simulation results are given to show the effectiveness and benefits of the proposed methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61473070, 61433004), the Fundamental Research Funds for the Central Universities (Grant Nos. N130504002 and N130104001), and SAPI Fundamental Research Funds (Grant No. 2013ZCX01).

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Authors and Affiliations

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

    Sanbo Ding, Zhanshan Wang, Zhanjun Huang & Huaguang Zhang

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  1. Sanbo Ding
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  2. Zhanshan Wang
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  3. Zhanjun Huang
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  4. Huaguang Zhang
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Correspondence to Zhanshan Wang.

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Ding, S., Wang, Z., Huang, Z. et al. Novel Switching Jumps Dependent Exponential Synchronization Criteria for Memristor-Based Neural Networks. Neural Process Lett 45, 15–28 (2017). https://doi.org/10.1007/s11063-016-9504-3

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