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Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing

หน่วยงาน Nanyang Technological University, Singapore

รายละเอียด

ชื่อเรื่อง : Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing
นักวิจัย : Ni, Wangdong , Tan, Soon Keat , Ng, Wun Jern , Brown, Steven D.
คำค้น : DRNTU::Engineering::Civil engineering::Water resources.
หน่วยงาน : Nanyang Technological University, Singapore
ผู้ร่วมงาน : -
ปีพิมพ์ : 2555
อ้างอิง : Ni, W., Tan, S. K., Ng, W. J., & Brown, S. D. (2012). Moving-Window GPR for Nonlinear Dynamic System Modeling with Dual Updating and Dual Preprocessing. Industrial & Engineering Chemistry Research, 51(18), 6416-6428. , 0888-5885 , http://hdl.handle.net/10220/16749 , http://dx.doi.org/10.1021/ie201898a
ที่มา : -
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Industrial & engineering chemistry research
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

The characteristics of nonlinearity and time-varying changes in most industrial processes usually cripple the predictive performance of conventional soft sensors. In this article, moving-window Gaussian process regression (MWGPR) is proposed to effectively capture the process dynamics and to model nonlinearity simultaneously. Applications of the proposed MWGPR method to the modeling of the activity of a catalyst and an industrial propylene polymerization process are presented. The results clearly demonstrate that the MWGPR method effectively tracks the process changes to generate satisfactory predictive performance. Two modeling strategies, namely, dual updating and dual preprocessing, are applied to MWGPR, in an attempt to more efficiently track the process dynamics. Dual updating takes into account both the time-varying variance of the process and the bias between the actual measurement and the model prediction. The improvement in performance is illustrated by a case study on modeling catalyst activity. Simultaneous removal of embedded noise in both process parameters and process output variables by dual preprocessing could significantly improve the predictive capability of MWGPR, as illustrated by the performance of a modeling study of an industrial propylene polymerization process.

บรรณานุกรม :
Ni, Wangdong , Tan, Soon Keat , Ng, Wun Jern , Brown, Steven D. . (2555). Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing.
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Ni, Wangdong , Tan, Soon Keat , Ng, Wun Jern , Brown, Steven D. . 2555. "Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing".
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Ni, Wangdong , Tan, Soon Keat , Ng, Wun Jern , Brown, Steven D. . "Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing."
    กรุงเทพมหานคร : Nanyang Technological University, Singapore, 2555. Print.
Ni, Wangdong , Tan, Soon Keat , Ng, Wun Jern , Brown, Steven D. . Moving-window GPR for nonlinear dynamic system modeling with dual updating and dual preprocessing. กรุงเทพมหานคร : Nanyang Technological University, Singapore; 2555.