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Robust passivity analysis of a class of discrete-time stochastic neural networks

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Abstract

This paper addresses the passivity problem of a class of discrete-time stochastic neural networks with time-varying delays and norm-bounded parameter uncertainties. New delay-dependent passivity conditions are obtained by using a novel Lyapunov functional together with the linear matrix inequality approach. Numerical examples show the effectiveness of the proposed method.

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Acknowledgments

This work is supported by the Graduate Innovation and Creativity Foundation of Jiangsu Province under Grant CXZZ11 0255.

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Authors and Affiliations

  1. School of Information Science and Engineering, Changzhou University, Changzhou, 213164, People’s Republic of China

    Guodong Shi

  2. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Qian Ma & Yi Qu

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  1. Guodong Shi
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  2. Qian Ma
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  3. Yi Qu
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Correspondence to Qian Ma.

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Shi, G., Ma, Q. & Qu, Y. Robust passivity analysis of a class of discrete-time stochastic neural networks. Neural Comput & Applic 22, 1509–1517 (2013). https://doi.org/10.1007/s00521-012-0838-0

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  • DOI: https://doi.org/10.1007/s00521-012-0838-0

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