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Robust dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties

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Abstract

This paper investigates the problem of robust dissipativity analysis for uncertain neural networks with time-varying delay. The norm-bounded uncertainties enter into the neural networks in randomly ways, and such randomly occurring uncertainties (ROUs) obey certain mutually uncorrelated Bernoulli distributed white noise sequences. By employing the linear matrix inequality (LMI) approach, a sufficient condition is established to ensure the robust stochastic stability and dissipativity of the considered neural networks. Some special cases are also considered. Two numerical examples are given to demonstrate the validness and the less conservatism of the obtained results.

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Acknowledgements

The work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (2010-0009373). This work was also supported in part by the National Creative Research Groups Science Foundation of China under Grant 60721062, the National High Technology Research and Development Program of China 863 Program under Grant 2006AA04 Z182, and the National Natural Science Foundation of China under Grants 60736021 and 61174029.

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Authors and Affiliations

  1. Department of Electrical Engineering, Yeungnam University, 214-1 Dae-Dong, Kyongsan, 712-749, Republic of Korea

    Zheng-Guang Wu & Ju H. Park

  2. National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang, 310027, P.R. China

    Zheng-Guang Wu, Hongye Su & Jian Chu

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  1. Zheng-Guang Wu
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  2. Ju H. Park
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  3. Hongye Su
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  4. Jian Chu
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Correspondence to Ju H. Park.

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Wu, ZG., Park, J.H., Su, H. et al. Robust dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties. Nonlinear Dyn 69, 1323–1332 (2012). https://doi.org/10.1007/s11071-012-0350-1

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  • DOI: https://doi.org/10.1007/s11071-012-0350-1

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