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Exponential Stability of Switched Time-varying Delayed Neural Networks with All Modes Being Unstable

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Abstract

This paper aims to design an appropriate switching law to stabilize the switched neural networks with time-varying delays when all subsystems are unstable. By using the discretized Lyapunov function approach and the extended comparison principle for impulsive systems, the stability of switched delayed neural networks composed full of unstable subsystems is analyzed and a computable sufficient condition is derived in the framework of dwell time. The effectiveness of the proposed results is illustrated by a numerical example.

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Acknowledgments

This publication was made possible by NPRP Grant No. NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. This work was also supported by Natural Science Foundation of China (Grant Nos: 61374078, 61403313).

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Jiangtao Qi, Chuandong Li & Wei Zhang

  2. Texas A & M University at Qatar, Doha, 5825, Qatar

    Tingwen Huang

Authors
  1. Jiangtao Qi
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  2. Chuandong Li
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  3. Tingwen Huang
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  4. Wei Zhang
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Correspondence to Chuandong Li.

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Qi, J., Li, C., Huang, T. et al. Exponential Stability of Switched Time-varying Delayed Neural Networks with All Modes Being Unstable. Neural Process Lett 43, 553–565 (2016). https://doi.org/10.1007/s11063-015-9428-3

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  • DOI: https://doi.org/10.1007/s11063-015-9428-3

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