3 Scopus citations

Abstract

An important part of system modeling involves establishing relations of system variables. Data collected from a system reflects relations of system variables and thus allows us to learn variable relations from system data. Existing machine learning and data mining techniques focus on learning variable relations that hold for all values of variables. However, different variable relations may exist for different ranges of variable values, or a variable relation holds only for certain ranges of variable values but not for full ranges of variable values. This paper presents the use of a new algorithm, called Partial-Value Association Discovery (PVAD), to learn partial-value variable relations for energy consumption system modeling and engineering retention.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages368-372
Number of pages5
ISBN (Electronic)9781538663387
DOIs
StatePublished - Jun 13 2018
Event4th International Conference on Control, Automation and Robotics, ICCAR 2018 - Auckland, New Zealand
Duration: Apr 20 2018Apr 23 2018

Other

Other4th International Conference on Control, Automation and Robotics, ICCAR 2018
CountryNew Zealand
CityAuckland
Period4/20/184/23/18

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Keywords

  • data mining
  • machine learning
  • system modeling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization

Cite this

Ye, N., Fok, T. Y., Wang, X., Collofello, J., & Dickson, N. (2018). Learning partial-value variable relations for system modeling. In Proceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018 (pp. 368-372). [8384702] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCAR.2018.8384702