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Further results on passivity analysis of delayed cellular neural networks

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  • Park, Ju H.

Abstract

The passivity condition for delayed neural networks with uncertainties is considered in this article. From simple extension of a recent work for stability analysis of the system, a new criterion for the passivity of the system is derived in terms of linear matrix inequalities (LMIs), which can be easily solved by using various convex optimization algorithms. A numerical example is given to show the usefulness of our result.

Suggested Citation

  • Park, Ju H., 2007. "Further results on passivity analysis of delayed cellular neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 34(5), pages 1546-1551.
    • Handle: RePEc:eee:chsofr:v:34:y:2007:i:5:p:1546-1551
    DOI: 10.1016/j.chaos.2005.04.124
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    Cited by:

    1. Rihan, F.A. & Abdel Rahman, D.H. & Lakshmanan, S. & Alkhajeh, A.S., 2014. "A time delay model of tumour–immune system interactions: Global dynamics, parameter estimation, sensitivity analysis," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 606-623.
    2. Singh, Vimal, 2009. "Novel global robust stability criterion for neural networks with delay," Chaos, Solitons & Fractals, Elsevier, vol. 41(1), pages 348-353.
    3. Ye, Zhiyong & Ji, Huihui & Zhang, He, 2016. "Passivity analysis of Markovian switching complex dynamic networks with multiple time-varying delays and stochastic perturbations," Chaos, Solitons & Fractals, Elsevier, vol. 83(C), pages 147-157.
    4. 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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