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Robust passivity analysis for stochastic impulsive neural networks with leakage and additive time-varying delay components

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  • Samidurai, Rajendran
  • Manivannan, Raman

Abstract

The purpose of this paper is to investigate the problem of robust passivity analysis for delayed stochastic impulsive neural networks with leakage and additive time-varying delays. The novel contribution of this paper lies in the consideration of a new integral inequality proved to be well-known Jensen’s inequality and takes fully the relationship between the terms in the Leibniz–Newton formula within the framework of linear matrix inequalities (LMIs). By constructing a suitable Lyapunov–Krasovskii functional with triple and four integral terms using Jensen’s inequality, integral inequality technique and LMI frame work, which guarantees stability for the passivity of addressed neural networks. This LMI can be easily solved via convex optimization techniques. Finally, two interesting numerical examples are given to show the effectiveness of the theoretical results.

Suggested Citation

  • Samidurai, Rajendran & Manivannan, Raman, 2015. "Robust passivity analysis for stochastic impulsive neural networks with leakage and additive time-varying delay components," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 743-762.
    • Handle: RePEc:eee:apmaco:v:268:y:2015:i:c:p:743-762
    DOI: 10.1016/j.amc.2015.06.116
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    References listed on IDEAS

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    1. Song, Qiankun & Wang, Zidong, 2008. "Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3314-3326.
    2. R. Sakthivel & R. Samidurai & S. M. Anthoni, 2010. "Asymptotic Stability of Stochastic Delayed Recurrent Neural Networks with Impulsive Effects," Journal of Optimization Theory and Applications, Springer, vol. 147(3), pages 583-596, December.
    3. P. Balasubramaniam & G. Nagamani, 2011. "Global Robust Passivity Analysis for Stochastic Interval Neural Networks with Interval Time-Varying Delays and Markovian Jumping Parameters," Journal of Optimization Theory and Applications, Springer, vol. 149(1), pages 197-215, April.
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    Cited by:

    1. Nagamani, G. & Ramasamy, S., 2016. "Stochastic dissipativity and passivity analysis for discrete-time neural networks with probabilistic time-varying delays in the leakage term," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 237-257.
    2. Cao, Yang & Samidurai, R. & Sriraman, R., 2019. "Robust passivity analysis for uncertain neural networks with leakage delay and additive time-varying delays by using general activation function," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 57-77.
    3. Li, Lingchun & Shen, Mouquan & Zhang, Guangming & Yan, Shen, 2017. "H∞ control of Markov jump systems with time-varying delay and incomplete transition probabilities," Applied Mathematics and Computation, Elsevier, vol. 301(C), pages 95-106.

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