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Further results on dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties

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Abstract

In this paper, the problem of robust dissipativity is investigated for neural networks with both time-varying delay and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to obey mutually uncorrelated Bernoulli-distributed white noise sequences. By constructing a new Lyapunov–Krasovskii functional, some improved delay-dependent dissipativity conditions are derived based on two integral inequalities, which are formulated in terms of linear matrix inequality. Furthermore, some information of activation function ignored in previous works has been taken into account in the resulting condition. The effectiveness and the improvement of the proposed approach are demonstrated by two illustrating numerical examples.

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Acknowledgments

This work of H.B. Zeng was supported by the National Natural Science Foundation of China under Grant Nos. 61304064 and 61273157. The work of J.H. Park was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2013R1A1A2A10005201). Special thanks of Dr. J.H. Park go to N. Kim for continuous supports and encouragement on his works.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, 412007, China

    Hong-Bing Zeng

  2. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan, 712-749, Republic of Korea

    Hong-Bing Zeng & Ju H. Park

  3. School of Mathematic Science, Liaocheng University, Liaocheng, 252000, Shandong, China

    Jian-Wei Xia

Authors
  1. Hong-Bing Zeng
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  2. Ju H. Park
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  3. Jian-Wei Xia
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Correspondence to Ju H. Park.

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Zeng, HB., Park, J.H. & Xia, JW. Further results on dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties. Nonlinear Dyn 79, 83–91 (2015). https://doi.org/10.1007/s11071-014-1646-0

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  • DOI: https://doi.org/10.1007/s11071-014-1646-0

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