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New delay-dependent criterion for the stability of recurrent neural networks with time-varying delay

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Abstract

This paper is concerned with the global asymptotic stability of a class of recurrent neural networks with interval time-varying delay. By constructing a suitable Lyapunov functional, a new criterion is established to ensure the global asymptotic stability of the concerned neural networks, which can be expressed in the form of linear matrix inequality and independent of the size of derivative of time varying delay. Two numerical examples show the effectiveness of the obtained results.

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Authors and Affiliations

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

    HuaGuang Zhang & ZhanShan Wang

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  1. HuaGuang Zhang
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  2. ZhanShan Wang
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Correspondence to HuaGuang Zhang.

Additional information

Supported by the National Natural Science Foundation of China (Grant Nos. 60534010, 60728307, 60774048, 60774093), the Program for Cheung Kong Scholars and Innovative Research Groups of China (Grant No. 60521003) and the National High-Tech Research & Development Program of China (Grant No. 2006AA04Z183), China Postdoctoral Sciencer Foundation (Grant No. 20080431150), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 200801451096)

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Zhang, H., Wang, Z. New delay-dependent criterion for the stability of recurrent neural networks with time-varying delay. Sci. China Ser. F-Inf. Sci. 52, 942–948 (2009). https://doi.org/10.1007/s11432-009-0100-2

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  • DOI: https://doi.org/10.1007/s11432-009-0100-2

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