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Two algebraic criteria for input-to-state stability of recurrent neural networks with time-varying delays

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Abstract

This paper presents two algebraic criteria for the input-to-state stability of recurrent neural networks with time-varying delays. The criteria which also ensure global exponential stability when the input u(t) is equal to 0 and is easy to be verified only with the connection weights of the recurrent neural networks. Two numerical examples are given to demonstrate the effectiveness of the proposed criteria.

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Acknowledgments

The authors would like to thank the editor-in-chief Professor John MacIntyre and the referees for their detailed comments and valuable suggestions, which considerably improved the presentation of this paper. This work was supported by the National Science Foundation of China with Grant Nos. 60740430664, 11101434, 61005089 and 51104157 and the Fundamental Research Funds for the Central Universities JGK101677.

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Authors and Affiliations

  1. College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China

    Song Zhu

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yi Shen

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  1. Song Zhu
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  2. Yi Shen
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Correspondence to Song Zhu.

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Zhu, S., Shen, Y. Two algebraic criteria for input-to-state stability of recurrent neural networks with time-varying delays. Neural Comput & Applic 22, 1163–1169 (2013). https://doi.org/10.1007/s00521-012-0882-9

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  • DOI: https://doi.org/10.1007/s00521-012-0882-9

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