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Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

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  • Yang Li
  • Hua-Liang Wei
  • Stephen. A. Billings
  • P.G. Sarrigiannis

Abstract

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.

Suggested Citation

  • Yang Li & Hua-Liang Wei & Stephen. A. Billings & P.G. Sarrigiannis, 2016. "Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(11), pages 2671-2681, August.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:11:p:2671-2681
    DOI: 10.1080/00207721.2015.1014448
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    Cited by:

    1. Yuanlin Gu & Hua-Liang Wei, 2018. "Significant Indicators and Determinants of Happiness: Evidence from a UK Survey and Revealed by a Data-Driven Systems Modelling Approach," Social Sciences, MDPI, vol. 7(4), pages 1-12, March.

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