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Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects

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Abstract

In this paper, a generalized memristor-based recurrent neural network model with variable delays and impulse effects is considered. By using an impulsive delayed differential inequality and Lyapunov function, the exponential stability of the impulsive delayed memristor-based recurrent neural networks is investigated. Several exponential and uniform stability criteria of this impulsive delayed system are derived, which promotes the study of memristor-based recurrent neural networks. Finally, the effectiveness of obtained results is illustrated by two numerical examples.

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Acknowledgments

The work was supported by Program for New Century Excellent Talents in University (Grant No. [2013]47), National Natural Science Foundation of China (Grant Nos. 61372139, 61374078, 61503175, 61571372, 61101233, 60972155), Spring Sunshine Plan Research Project of Ministry of Education of China (Grant No. z2011148), Fundamental Research Funds for the Central Universities (Grant Nos. XDJK2014A009, XDJK2016A001), 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). High School Key Scientific Research Project of Henan Province (Grant No. 15A120013), NPRP grant ♯ 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, China

    Huamin Wang, Shukai Duan, Chuandong Li & Lidan Wang

  2. Department of Mathematics, Luoyang Normal University, Luoyang, 471022, Henan, 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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  3. Chuandong Li
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  4. Lidan Wang
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  5. Tingwen Huang
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Correspondence to Shukai Duan.

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Wang, H., Duan, S., Li, C. et al. Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects. Neural Comput & Applic 28, 669–678 (2017). https://doi.org/10.1007/s00521-015-2094-6

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  • DOI: https://doi.org/10.1007/s00521-015-2094-6

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