NDLI: kNN-RVM lazy learning approach for soft-sensing modeling of fed-batch processes

Content Provider	IEEE Xplore Digital Library
Author	Jun Ji Hai-qing Wang Kun Chen Dian-cai Yang
Copyright Year	2011
Description	Author affiliation: MESNAC, National Tire Process and Control Engineering Research Center, Qingdao, China (Dian-cai Yang) \|\| State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, 310027, China (Jun Ji; Hai-qing Wang; Kun Chen)
Abstract	Fed-batch processes are inherently difficult to model owing to non-steady-state operation, small-sample condition, instinct time-variation and batch-to-batch variation caused by drifting. Furthermore, when the process switches to different operation phrases, global learning modeling methods would suffer poor performance due to the negative impact of overdue training samples. In this paper, a k nearest neighbor relevance vector machine (kNN-RVM) based lazy learning method is proposed to model the fed-batch processes to soft-sense the corresponding production indices. A recursive algorithm is developed to effectively obtain the kernel matrices used by previous kNN step and following modeling process. Simulative soft-sensors of penicillin production process and rubber mixing process are implemented to valid the proposed method. Comparative results indict that proposed method has better precision and much lower computational complexity than relevance vector machine (RVM) on soft-sensing modeling of fed-batch processes.
Starting Page	272
Ending Page	276
File Size	363487
Page Count	5
File Format	PDF
ISBN	9781424474608
e-ISBN	9789881725509
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Zhejiang University
Subject Keyword	Training Support vector machines Computational modeling Process control Production Rubber Kernel
Content Type	Text
Resource Type	Article

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