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Dynamical analysis of uncertain neural networks with multiple time delays

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  • Sabri Arik

Abstract

This paper investigates the robust stability problem for dynamical neural networks in the presence of time delays and norm-bounded parameter uncertainties with respect to the class of non-decreasing, non-linear activation functions. By employing the Lyapunov stability and homeomorphism mapping theorems together, a new delay-independent sufficient condition is obtained for the existence, uniqueness and global asymptotic stability of the equilibrium point for the delayed uncertain neural networks. The condition obtained for robust stability establishes a matrix–norm relationship between the network parameters of the neural system, which can be easily verified by using properties of the class of the positive definite matrices. Some constructive numerical examples are presented to show the applicability of the obtained result and its advantages over the previously published corresponding literature results.

Suggested Citation

  • Sabri Arik, 2016. "Dynamical analysis of uncertain neural networks with multiple time delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(3), pages 730-739, February.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:3:p:730-739
    DOI: 10.1080/00207721.2014.902158
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    Cited by:

    1. Li, Li & Wang, Zhen & Li, Yuxia & Shen, Hao & Lu, Junwei, 2018. "Hopf bifurcation analysis of a complex-valued neural network model with discrete and distributed delays," Applied Mathematics and Computation, Elsevier, vol. 330(C), pages 152-169.
    2. Subramanian, K. & Muthukumar, P. & Lakshmanan, S., 2018. "State feedback synchronization control of impulsive neural networks with mixed delays and linear fractional uncertainties," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 267-281.
    3. Mingwen Zheng & Lixiang Li & Haipeng Peng & Jinghua Xiao & Yixian Yang & Yanping Zhang & Hui Zhao, 2018. "Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
    4. Thoiyab, N. Mohamed & Muruganantham, P. & Zhu, Quanxin & Gunasekaran, Nallappan, 2021. "Novel results on global stability analysis for multiple time-delayed BAM neural networks under parameter uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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