Robust Dynamic Mode Decomposition

Hossein Abolmasoumi, Amir; Netto, Marcos; Mili, Lamine

doi:10.1109/access.2022.3183760

Robust Dynamic Mode Decomposition

Journal Article · Thu Jun 16 00:00:00 EDT 2022 · IEEE Access

DOI:https://doi.org/10.1109/access.2022.3183760· OSTI ID:1872670

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Arak University (Iran)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

This paper develops a robust dynamic mode decomposition (RDMD) method endowed with statistical and numerical robustness. Statistical robustness ensures estimation efficiency at the Gaussian and non-Gaussian probability distributions, including heavy-tailed distributions. The proposed RDMD is statistically robust because the outliers in the data set are flagged via projection statistics and suppressed using a Schweppe-type Huber generalized maximum-likelihood estimator that minimizes a convex Huber cost function. The latter is solved using the iteratively reweighted least-squares algorithm that is known to exhibit an excellent convergence property and numerical stability than the Newton algorithms. Several numerical simulations using canonical models of dynamical systems demonstrate the excellent performance of the proposed RDMD method. The results reveal that it outperforms several other methods proposed in the literature.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1872670

Alternate ID(s):: OSTI ID: 1873781; OSTI ID: 1880331

Report Number(s):: NREL/JA-5D00-79972; MainId:41177; UUID:0ce6763b-8174-4bd4-9c07-a875f7be97fd; MainAdminID:65042

Journal Information:: IEEE Access, Vol. 10; ISSN 2169-3536

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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