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Improved delay-dependent stability conditions for recurrent neural networks with multiple time-varying delays

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Abstract

This paper investigates the global asymptotic stability problem for recurrent neural networks with multiple time-varying delays. Using the free-weighting matrix technique, and incorporating the interconnected information between the upper bounds of multiple time-varying delays, two less conservative delay-dependent asymptotic stability conditions are proposed, which are expressed by linear matrix inequalities, and can be conveniently solved by the existing softwares. Numerical examples show the reduce conservatism of the obtained conditions.

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Acknowledgments

This work was supported by the National Natural Science Foundations of PR China under Grants 61304061, 61273119 and 61174076. This work was also partly supported by the Basic research Program of Natural Science of Henan Institute of Science and Technology under Grant 2013019.

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Authors and Affiliations

  1. School of Mathematical Sciences, Henan Institute of Science and Technology, Xinxiang, 453003, People’s Republic of China

    Yonggang Chen

  2. Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing, 210096, People’s Republic of China

    Shumin Fei

  3. School of Science, Huzhou Teachers College, Huzhou, 313000, People’s Republic of China

    Yongmin Li

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  1. Yonggang Chen
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  2. Shumin Fei
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  3. Yongmin Li
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Correspondence to Yonggang Chen.

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Chen, Y., Fei, S. & Li, Y. Improved delay-dependent stability conditions for recurrent neural networks with multiple time-varying delays. Nonlinear Dyn 78, 803–812 (2014). https://doi.org/10.1007/s11071-014-1478-y

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  • DOI: https://doi.org/10.1007/s11071-014-1478-y

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