Evidence map›Paper›PMID 29546254›Full record

ArticleIFAC-PapersOnLine2017

System Identification Algorithm for Non-Uniformly Sampled Data.

Korkut Bekiroglu, Constantino Lagoa, Stephanie T Lanza, Mario Sznaier

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IFAC-PapersOnLine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Korkut BekirogluThe School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore (korkutbekiroglu@gmail.com).
Constantino LagoaDepartment of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802 USA (lagoa@engr.psu.edu).
Stephanie T LanzaDepartment of Biobehavioral Health and The Methodology Center, The Pennsylvania State University, University Park, PA, 16802 USA (SLanza@psu.edu).
Mario SznaierDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA (msznaier@coe.neu.edu).

Funding

Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, RUNZE · 2015 to 2019
$13.9M
NIDA NIH HHS P50 DA039838
6 · The paper itself

Abstract

Considerable effort has been devoted to the development of algorithms for identification of parsimonious discrete time models from noisy input/output data sets since this facilitates controller design. Several methods, such as nuclear norm minimization, have been used to provide approximate solutions to this non-convex problem. However, even though the field of continuous time system identification is now mature, results on parsimonious model identification of continuous time systems are still very limited. In this paper, an atomic norm minimization method is proposed for this purpose that can handle non-uniformly sampled data without preprocessing. The proposed approach provides an efficient way to use noisy, non-uniformly sampled data to determine a reliable, low-order continuous time model. Numerical performance is illustrated using academic examples and simulated behavioral data from a smoking cessation study.

Indexed as

Continuous time system identificationnon-uniformly sampled dataparsimonious system identificationrandomized system identification algorithm

Identifiers

PMID29546254
PMCPMC5846195

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.