Software package for mosaic-Hankel structured low-rank approximation

Usevich, Konstantin; Markovsky, Ivan

doi:10.1109/cdc40024.2019.9028867

Cited by 1 publication

(3 citation statements)

References 29 publications

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“…Framing the problem in this way leads to the exciting prospect of applying existing SLRA tools and methods. 14,15 The algorithm described below uses the "variable projection method," but other methods exists such as those based on nuclear norm minimization. 17 Moreover, future approaches could take advantage of software that accounts for missing data.…”

Section: Problem Reformulationmentioning

confidence: 99%

“…17 Moreover, future approaches could take advantage of software that accounts for missing data. 14,15…”

Section: Problem Reformulationmentioning

confidence: 99%

“…The first step consists of the subspace method mentioned above, which leads to a good initial estimate of the parameters that define the system. The second step is a refinement of this initial estimate through the consideration of a structured low-rank approximation (SLRA) problem 14,15 corresponding to system identification. Both of these methods are tailored to the particular nature of the prior information.…”

Section: Introductionmentioning

confidence: 99%

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MIMO system identification using common denominator and numerators with known degrees

Fazzi

Großmann

Mercère

et al. 2022

Adaptive Control & Signal

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Summary In system identification, prior knowledge about the model structure may be available. However, imposing this structure on the identified model may be nontrivial. A new discrete‐time linear time‐invariant identification method is presented in the article that imposes prior knowledge of the degree of the common denominator of the system's transfer function matrix and the degrees of the numerators. First, a method is outlined for the solution in case of exact data. Then, this method is extended for noisy data in the output error setting. An initial estimate obtained by a subspace method is improved by a structured low‐rank approximation method. The performance of the method imposing the structure is compared on simulated data with the performance of classical identification methods that do not impose the structure.

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Section: Problem Reformulationmentioning

confidence: 99%

“…17 Moreover, future approaches could take advantage of software that accounts for missing data. 14,15…”

Section: Problem Reformulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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