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Novel Stability Criteria for Impulsive Memristive Neural Networks with Time-Varying Delays

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Abstract

In order to improve the ability of resisting disturbance for memristive neural networks (MNNs), a general impulsive controlled MNN with variable delays is constructed in this paper. Then, its dynamical behaviors are investigated based on the differential inclusion theories. By constructing suitable Lyapunov–Krasovskii-type functional and combining with integral and monotone function method, the global exponential stability criteria of delayed memristive neural networks (DMNNs) with impulse effects are derived. Furthermore, the impulsive controlled DMNNs can be transformed to DMNNs without impulsive effects, which promote and enrich the theoretical research results of MNNs. Finally, the effectiveness of obtained results is illustrated by two numerical examples.

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Acknowledgments

The work was supported by National Natural Science Foundation of China (Grant Nos. 61372139, 61503175, 61374078, 61571372, 61101233, 60972155), Program for New Century Excellent Talents in University (Grant Nos.[2013]47), “Spring Sunshine Plan” Research Project of Ministry of Education of China (Grant No. z2011148), Fundamental Research Funds for the Central Universities (Grant Nos. XDJK2016A001, XDJK2014A009), Program for Excellent Talents in scientific and technological activities for Overseas Scholars, Ministry of Personnel in China (Grant No. 2012-186), University Excellent Talents Supporting Foundations in of Chongqing (Grant No. 2011-65), University Key Teacher Supporting Foundations of Chongqing (Grant No. 2011-65) and High School Key Scientific Research Project of Henan Province (Grant No. 15A120013). This publication was made possible by NPRP grant \(\sharp \) NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation).

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People’s Republic of China

    Huamin Wang, Shukai Duan, Chuandong Li & Lidan Wang

  2. Department of Mathematics, Luoyang Normal University, Luoyang, 471022, Henan, People’s Republic of China

    Huamin Wang

  3. Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, 23874, Qatar

    Tingwen Huang

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  1. Huamin Wang
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  2. Shukai Duan
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Correspondence to Shukai Duan.

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Wang, H., Duan, S., Huang, T. et al. Novel Stability Criteria for Impulsive Memristive Neural Networks with Time-Varying Delays. Circuits Syst Signal Process 35, 3935–3956 (2016). https://doi.org/10.1007/s00034-015-0240-0

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