Highly efficient moving data window iterative identification for multiple-input multiple-output systems with colored noise

doi:10.1016/j.jfranklin.2026.108480

Highly efficient moving data window iterative identification for multiple-input multiple-output systems with colored noise

2026-05-01

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DOI：10.1016/j.jfranklin.2026.108480

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Abstract

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This paper investigates the identification issue of multivariable ARX systems with colored noise. To address the bias caused by colored noise, a data filtering method is applied to whiten the original multivariable system, which filters the input-output data without altering their inherent dynamics and yields a filtered identification model. Considering the computational complexity and burden in multivariable system identification, a three-stage filtered stochastic gradient algorithm is proposed based on the filtered identification model with a hierarchical strategy. In addition, the historical innovations are utilized to further improve estimation accuracy and convergence performance, resulting in a three-stage filtered multi-innovation stochastic gradient algorithm. The numerical examples verify the effectiveness of the proposed algorithms in identifying multivariable ARX systems.

Keywords:

Multivariable ARX systems

Colored noise

Data filtering

Stochastic gradient algorithm

System identification

Highly efficient moving data window iterative identification for multiple-input multiple-output systems with colored noise

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