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Robust stability criteria for uncertain stochastic neural networks of neutral-type with interval time-varying delays

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Abstract

This paper deals with the robust stability problem of uncertain stochastic neural networks of neutral-type with interval time-varying delays. The uncertainties under consideration are norm-bounded, and the delay is assumed to be time-varying and belongs to a given interval. By using the Lyapunov-Krasovskill functional method and the linear matrix inequality (LMI) technique, the novel stability criteria are derived in terms of LMI. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed criteria.

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Acknowledgments

The authors would like to thank the editor and two anonymous reviewers for their insightful comments and valuable suggestions. This work was supported by the National Natural Science Foundation (No. 60974090) and by the Fundamental Research Funds for the Central Universities (No. CDJXS11172237).

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Authors and Affiliations

  1. College of Automation, Chongqing University, Chongqing, 400044, P. R. China

    Guoquan Liu, Simon X. Yang, Yi Chai & Wei Fu

  2. School of Engineering, University of Guelph, Guelph, ON, N1G2W1, Canada

    Simon X. Yang

  3. Department of Computer Science, Chongqing Education College, Chongqing, 400067, China

    Wei Feng

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  1. Guoquan Liu
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  2. Simon X. Yang
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  3. Yi Chai
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  4. Wei Feng
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  5. Wei Fu
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Correspondence to Guoquan Liu.

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Liu, G., Yang, S.X., Chai, Y. et al. Robust stability criteria for uncertain stochastic neural networks of neutral-type with interval time-varying delays. Neural Comput & Applic 22, 349–359 (2013). https://doi.org/10.1007/s00521-011-0696-1

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  • DOI: https://doi.org/10.1007/s00521-011-0696-1

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