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Relaxed passivity conditions for discrete-time stochastic delayed neural networks

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Abstract

In this paper, the passivity problem is researched for discrete-time stochastic neural networks with time-varying delay. By utilizing a novel Lyapunov–Krasovskii functional, delay-decomposition method and reciprocally convex approach, some sufficient delay-dependent passivity conditions are established in the form of linear matrix inequalities. Furthermore, these new criteria do not require all the symmetric matrices involved in the employed Lyapunov–Krasovskii functional to be positive definite. Finally, three numerical examples are given to illustrate the reduced conservatism and effectiveness of the proposed method.

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Acknowledgments

National Natural Science Foundation of China (61273015). The natural science research project of Fuyang Normal College(2013FSKJ09).

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Authors and Affiliations

  1. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Wei Kang & Shouming Zhong

  2. School of Information Engineering, Fuyang Normal College, Fuyang, 236041, People’s Republic of China

    Wei Kang

  3. School of Science, Hubei University for Nationalities, Enshi, 44500, People’s Republic of China

    Jun Cheng

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  1. Wei Kang
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  2. Shouming Zhong
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  3. Jun Cheng
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Correspondence to Wei Kang.

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Kang, W., Zhong, S. & Cheng, J. Relaxed passivity conditions for discrete-time stochastic delayed neural networks. Int. J. Mach. Learn. & Cyber. 7, 205–216 (2016). https://doi.org/10.1007/s13042-015-0428-4

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  • DOI: https://doi.org/10.1007/s13042-015-0428-4

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