System identification under Lebesgue sampling and its asymptotic property

Takahiro Kawaguchi, Sosaburo Hikono, Ichiro Maruta, Shuichi Adachi

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2016 IEEE 55th Conference on Decision and Control, CDC 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2079-2084
ページ数6
ISBN(電子版)9781509018376
DOI
出版ステータスPublished - 2016 12 27
イベント55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
継続期間: 2016 12 122016 12 14

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
CountryUnited States
CityLas Vegas
Period16/12/1216/12/14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

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