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Global stability analysis of multitime-scale neural networks

  • ISNN 2011
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Abstract

Global asymptotic stability problem is studied for a class of recurrent neural networks with multitime scale. The concerned network involves two coupling terms, i.e., long-term memory and short-term memory, which leads to the difficulty to the dynamics analysis, especially for the case of multiple time varying delays. Some novel stability criteria are proposed on the basis of linear matrix inequality technique for the concerned neural network, which sufficiently consider the inhibitory actions in the different memories. From the viewpoint of biological information, the proposed results obviously improve the existing stability criteria. A numerical example is used to show the effectiveness of the obtained results.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grants 61074073 and 61034005, the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 200801451096, the Postdoctoral Science Foundation of China under Grant 200902547 and the Fundamental Research Funds for the Central Universities under Grants N090404017 and N100104102, and Program for New Century Excellent Talents in University of China (NCET-10-0306).

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Authors and Affiliations

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

    Zhanshan Wang, Enlin Zhang & Huaguang Zhang

  2. College of Information Science and Technology, Donghua University, Shanghai, China

    Zhengyun Ren

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  1. Zhanshan Wang
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  2. Enlin Zhang
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  3. Huaguang Zhang
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  4. Zhengyun Ren
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Correspondence to Huaguang Zhang or Zhengyun Ren.

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Wang, Z., Zhang, E., Zhang, H. et al. Global stability analysis of multitime-scale neural networks. Neural Comput & Applic 22, 211–217 (2013). https://doi.org/10.1007/s00521-011-0680-9

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  • DOI: https://doi.org/10.1007/s00521-011-0680-9

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