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Robustness analysis of a hybrid of recursive neural dynamics for online matrix inversion

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  • Chen, Ke
  • Yi, Chenfu

Abstract

Encouraged by superior convergence performance achieved by a recently-proposed hybrid of recursive neural dynamics for online matrix inversion, we investigate its robustness properties in this paper when there exists large model implementation errors. Theoretical analysis shows that the perturbed dynamic system is still global stable with the tight steady-state bound of solution error estimated. Moreover, this paper analyses global exponential convergence rate and finite convergence time of such a hybrid dynamical model to a relatively loose solution error bound. Computer simulation results substantiate our analysis on the perturbed hybrid neural dynamics for online matrix inversion when having large implementation errors.

Suggested Citation

  • Chen, Ke & Yi, Chenfu, 2016. "Robustness analysis of a hybrid of recursive neural dynamics for online matrix inversion," Applied Mathematics and Computation, Elsevier, vol. 273(C), pages 969-975.
    • Handle: RePEc:eee:apmaco:v:273:y:2016:i:c:p:969-975
    DOI: 10.1016/j.amc.2015.10.026
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    References listed on IDEAS

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    1. Fiori, Simone, 2015. "Kolmogoroff–Nagumo mean over the affine symplectic group of matrices," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 820-837.
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    Cited by:

    1. Hadeel Alharbi & Obaid Alshammari & Houssem Jerbi & Theodore E. Simos & Vasilios N. Katsikis & Spyridon D. Mourtas & Romanos D. Sahas, 2023. "A Fresnel Cosine Integral WASD Neural Network for the Classification of Employee Attrition," Mathematics, MDPI, vol. 11(6), pages 1-17, March.

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