System identification under Lebesgue sampling and its asymptotic property

Takahiro Kawaguchi; Sosaburo Hikono; Ichiro Maruta; Shuichi Adachi

doi:10.1109/CDC.2016.7798570

System identification under Lebesgue sampling and its asymptotic property

Takahiro Kawaguchi, Sosaburo Hikono, Ichiro Maruta, Shuichi Adachi

Department of Applied Physics and Physico-Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In system identification, the more data is collected, the more accurate model is obtained. However, under limited communication bandwidth or computational resources, it is sometimes difficult to collect and store all the measured data, so it is desired to collect and store only useful data for improving model accuracy. This paper focuses on Lebesgue sampling, which uses thresholds on signal level for triggering sampler and proposes a system identification method with using information obtained in intervals between Lebesgue sampled data. The asymptotic variance of the estimated parameter is analyzed and effectiveness of the proposed method is examined through numerical examples.

Original language	English
Title of host publication	2016 IEEE 55th Conference on Decision and Control, CDC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2079-2084
Number of pages	6
ISBN (Electronic)	9781509018376
DOIs	https://doi.org/10.1109/CDC.2016.7798570
Publication status	Published - 2016 Dec 27
Event	55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: 2016 Dec 12 → 2016 Dec 14

Other

Other	55th IEEE Conference on Decision and Control, CDC 2016
Country/Territory	United States
City	Las Vegas
Period	16/12/12 → 16/12/14

ASJC Scopus subject areas

Artificial Intelligence
Decision Sciences (miscellaneous)
Control and Optimization

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10.1109/CDC.2016.7798570

Cite this

System identification under Lebesgue sampling and its asymptotic property. / Kawaguchi, Takahiro; Hikono, Sosaburo; Maruta, Ichiro et al.

2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2079-2084 7798570.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kawaguchi, T, Hikono, S, Maruta, I & Adachi, S 2016, System identification under Lebesgue sampling and its asymptotic property. in 2016 IEEE 55th Conference on Decision and Control, CDC 2016., 7798570, Institute of Electrical and Electronics Engineers Inc., pp. 2079-2084, 55th IEEE Conference on Decision and Control, CDC 2016, Las Vegas, United States, 16/12/12. https://doi.org/10.1109/CDC.2016.7798570

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author = "Takahiro Kawaguchi and Sosaburo Hikono and Ichiro Maruta and Shuichi Adachi",

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Y1 - 2016/12/27

N2 - In system identification, the more data is collected, the more accurate model is obtained. However, under limited communication bandwidth or computational resources, it is sometimes difficult to collect and store all the measured data, so it is desired to collect and store only useful data for improving model accuracy. This paper focuses on Lebesgue sampling, which uses thresholds on signal level for triggering sampler and proposes a system identification method with using information obtained in intervals between Lebesgue sampled data. The asymptotic variance of the estimated parameter is analyzed and effectiveness of the proposed method is examined through numerical examples.

AB - In system identification, the more data is collected, the more accurate model is obtained. However, under limited communication bandwidth or computational resources, it is sometimes difficult to collect and store all the measured data, so it is desired to collect and store only useful data for improving model accuracy. This paper focuses on Lebesgue sampling, which uses thresholds on signal level for triggering sampler and proposes a system identification method with using information obtained in intervals between Lebesgue sampled data. The asymptotic variance of the estimated parameter is analyzed and effectiveness of the proposed method is examined through numerical examples.

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ER -

System identification under Lebesgue sampling and its asymptotic property

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