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Time-varying matrix estimation in stochastic continuous-time models under coloured noise using LSM with forgetting factor

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  • J. Escobar
  • A. Poznyak

Abstract

In this article, we deal with time-varying matrix estimation in continuous-time stochastic models under a coloured noise. The suggested estimation algorithm is based on the least squares method (LSM) with forgetting factor. The forming filter generating this ‘coloured‘ noise from a standard ‘white noise’ is assumed to be partially known. An analysis of the convergence zone is presented. A numerical example illustrates the effectiveness of the proposed algorithm.

Suggested Citation

  • J. Escobar & A. Poznyak, 2011. "Time-varying matrix estimation in stochastic continuous-time models under coloured noise using LSM with forgetting factor," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(12), pages 2009-2020.
    • Handle: RePEc:taf:tsysxx:v:42:y:2011:i:12:p:2009-2020
    DOI: 10.1080/00207721003706852
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    Cited by:

    1. Jesica Escobar & Alexander Poznyak, 2022. "Robust Parametric Identification for ARMAX Models with Non-Gaussian and Coloured Noise: A Survey," Mathematics, MDPI, vol. 10(8), pages 1-38, April.
    2. Sergio Isai Palomino-Resendiz & Norma Beatriz Lozada-Castillo & Diego Alonso Flores-Hernández & Oscar Octavio Gutiérrez-Frías & Alberto Luviano-Juárez, 2021. "Adaptive Active Disturbance Rejection Control of Solar Tracking Systems with Partially Known Model," Mathematics, MDPI, vol. 9(22), pages 1-20, November.

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