System identification under Lebesgue sampling and its asymptotic property

Takahiro Kawaguchi, Sosaburo Hikono, Ichiro Maruta, Shuichi Adachi

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2079-2084
Number of pages6
ISBN (Electronic)9781509018376
DOIs
Publication statusPublished - 2016 Dec 27
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: 2016 Dec 122016 Dec 14

Other

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

    Fingerprint

ASJC Scopus subject areas

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

Cite this

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 (pp. 2079-2084). [7798570] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7798570