Feedback Control for Binary Response

Ping Yang Chen, Chi Chun Hsia, Yen Hao Su, Ray-Bing Chen, Sheng-Mao Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Defect rate control is crucial in industries. When binary response is considered, the defect rate is the average of these binary responses. In this study, with logistic regression model and sparsity assumption, the feedback control problem is expressed as an optimization problem which solves a hinge loss with an L1 penalty. Here the hinge loss function is substituted for the Huberized hinge loss to avoid discontinuity caused by the L1 penalty, and the majorization-minimization principle is applied to enhance computing efficiency. Then the coordinate descent algorithm is implemented for sparse estimation. Several examples and a real data example are used to illustrate the performance of the proposed feedback control procedure.

Original languageEnglish
Title of host publicationProceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages21-24
Number of pages4
ISBN (Electronic)9781538642030
DOIs
Publication statusPublished - 2018 May 9
Event2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 - Taipei, Taiwan
Duration: 2017 Dec 12017 Dec 3

Other

Other2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
CountryTaiwan
CityTaipei
Period17-12-0117-12-03

Fingerprint

Hinges
Feedback control
Defects
Logistics
Industry

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction

Cite this

Chen, P. Y., Hsia, C. C., Su, Y. H., Chen, R-B., & Chang, S-M. (2018). Feedback Control for Binary Response. In Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 (pp. 21-24). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAAI.2017.23
Chen, Ping Yang ; Hsia, Chi Chun ; Su, Yen Hao ; Chen, Ray-Bing ; Chang, Sheng-Mao. / Feedback Control for Binary Response. Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 21-24
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abstract = "Defect rate control is crucial in industries. When binary response is considered, the defect rate is the average of these binary responses. In this study, with logistic regression model and sparsity assumption, the feedback control problem is expressed as an optimization problem which solves a hinge loss with an L1 penalty. Here the hinge loss function is substituted for the Huberized hinge loss to avoid discontinuity caused by the L1 penalty, and the majorization-minimization principle is applied to enhance computing efficiency. Then the coordinate descent algorithm is implemented for sparse estimation. Several examples and a real data example are used to illustrate the performance of the proposed feedback control procedure.",
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Chen, PY, Hsia, CC, Su, YH, Chen, R-B & Chang, S-M 2018, Feedback Control for Binary Response. in Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017. Institute of Electrical and Electronics Engineers Inc., pp. 21-24, 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017, Taipei, Taiwan, 17-12-01. https://doi.org/10.1109/TAAI.2017.23

Feedback Control for Binary Response. / Chen, Ping Yang; Hsia, Chi Chun; Su, Yen Hao; Chen, Ray-Bing; Chang, Sheng-Mao.

Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 21-24.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Defect rate control is crucial in industries. When binary response is considered, the defect rate is the average of these binary responses. In this study, with logistic regression model and sparsity assumption, the feedback control problem is expressed as an optimization problem which solves a hinge loss with an L1 penalty. Here the hinge loss function is substituted for the Huberized hinge loss to avoid discontinuity caused by the L1 penalty, and the majorization-minimization principle is applied to enhance computing efficiency. Then the coordinate descent algorithm is implemented for sparse estimation. Several examples and a real data example are used to illustrate the performance of the proposed feedback control procedure.

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Chen PY, Hsia CC, Su YH, Chen R-B, Chang S-M. Feedback Control for Binary Response. In Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 21-24 https://doi.org/10.1109/TAAI.2017.23