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http://dx.doi.org/10.5391/JKIIS.2010.20.4.483

Modeling and Comparison for Auto-association using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR) in Online Monitoring Techniques  

Kim, Seong-Jun (강릉원주대학교 산업공학과)
Seo, In-Yong (한국전력공사 전력연구원)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.4, 2010 , pp. 483-488 More about this Journal
Abstract
An online monitoring based upon sensor system is essential to assure both efficient operation and safety in the power plant. Of great importance is modeling for auto-association (AA) in online monitoring technique. The objective of auto-associative models lies in predicting true values of plant operation parameters from sensor signals transmitted. This paper presents two AA models using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). The presented models are useful, in particular, when there are many parameters to monitor in the power plant. Illustrative examples are given by using a real-world plant dataset. AA performances of SVR and PLSR are finally summarized in terms of accuracy and sensitivity. According to our results, SVR shows much higher accuracy and, however, its sensitivity is relatively degraded.
Keywords
Online Monitoring; Auto-association; Support Vector Regression; Partial Least Square Regression; Accuracy; Sensitivity;
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Times Cited By KSCI : 5  (Citation Analysis)
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