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A based-on LMI stability criterion for delayed recurrent neural networks

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  • Liang, Jinling
  • Cao, Jinde

Abstract

This paper investigates the stability for a delayed recurrent neural network. Sufficient conditions are obtained for ascertaining the global asymptotic stability of the unique equilibrium of the network based on LMI technique. The results are computationally efficient, since they are in the form of linear matrix inequality (LMI), which can be checked easily by various recently developed convex optimization algorithms. Besides, the analysis approach allows one to consider different types of activation functions, such as piecewise linear, sigmoids with bounded activations as well as C1-smooth sigmoids. In the end of this paper two illustrative examples are also provided to show the effectiveness of our results.

Suggested Citation

  • Liang, Jinling & Cao, Jinde, 2006. "A based-on LMI stability criterion for delayed recurrent neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 28(1), pages 154-160.
    • Handle: RePEc:eee:chsofr:v:28:y:2006:i:1:p:154-160
    DOI: 10.1016/j.chaos.2005.04.120
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    Cited by:

    1. Singh, Vimal, 2007. "On global exponential stability of delayed cellular neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 33(1), pages 188-193.
    2. Singh, Vimal, 2009. "Remarks on estimating upper limit of norm of delayed connection weight matrix in the study of global robust stability of delayed neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 39(5), pages 2013-2017.
    3. Pharunyou Chanthorn & Grienggrai Rajchakit & Jenjira Thipcha & Chanikan Emharuethai & Ramalingam Sriraman & Chee Peng Lim & Raja Ramachandran, 2020. "Robust Stability of Complex-Valued Stochastic Neural Networks with Time-Varying Delays and Parameter Uncertainties," Mathematics, MDPI, vol. 8(5), pages 1-19, May.
    4. Singh, Vimal, 2007. "On global robust stability of interval Hopfield neural networks with delay," Chaos, Solitons & Fractals, Elsevier, vol. 33(4), pages 1183-1188.
    5. R. Saravanakumar & M. Syed Ali & Jinde Cao & He Huang, 2016. "state estimation of generalised neural networks with interval time-varying delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(16), pages 3888-3899, December.
    6. Song, Qiankun & Wang, Zidong, 2008. "Neural networks with discrete and distributed time-varying delays: A general stability analysis," Chaos, Solitons & Fractals, Elsevier, vol. 37(5), pages 1538-1547.
    7. Singh, Vimal, 2007. "Novel LMI condition for global robust stability of delayed neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 503-508.
    8. Singh, Vimal, 2007. "Some remarks on global asymptotic stability of neural networks with constant time delay," Chaos, Solitons & Fractals, Elsevier, vol. 32(5), pages 1720-1724.
    9. Singh, Vimal, 2007. "Global asymptotic stability of neural networks with delay: Comparative evaluation of two criteria," Chaos, Solitons & Fractals, Elsevier, vol. 31(5), pages 1187-1190.
    10. Singh, Vimal, 2009. "Novel global robust stability criterion for neural networks with delay," Chaos, Solitons & Fractals, Elsevier, vol. 41(1), pages 348-353.
    11. Singh, Vimal, 2007. "LMI approach to the global robust stability of a larger class of neural networks with delay," Chaos, Solitons & Fractals, Elsevier, vol. 32(5), pages 1927-1934.
    12. Singh, Vimal, 2007. "Simplified approach to the exponential stability of delayed neural networks with time varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 32(2), pages 609-616.
    13. Singh, Vimal, 2007. "Global robust stability of delayed neural networks: Estimating upper limit of norm of delayed connection weight matrix," Chaos, Solitons & Fractals, Elsevier, vol. 32(1), pages 259-263.
    14. Shan, Yaonan & She, Kun & Zhong, Shouming & Zhong, Qishui & Shi, Kaibo & Zhao, Can, 2018. "Exponential stability and extended dissipativity criteria for generalized discrete-time neural networks with additive time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 145-168.

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