NDLI: Adaptive local weighted kernel-based regression for online modeling of batch processes

Content Provider	IEEE Xplore Digital Library
Author	Kun Chen Haiqing Wang Jun Ji Zhihuan Song Yi Liu
Copyright Year	2010
Description	Author affiliation: State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, China (Kun Chen; Haiqing Wang; Jun Ji; Zhihuan Song) \|\| Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, China (Yi Liu)
Abstract	Fed-batch processes are inherently more difficult to characterize than continuous processes due to the variations under different operation stages, drifting and small-sample condition. The classical kernel-based regression (KR) methods, e.g., least squares support vector regression (LSSVR), aim to achieve a universal generalization performance, which may fail in some local regions when applied to batch process modeling. Local LSSVR model which only uses the neighbors of the query instance helps improve the accuracy, but it generally leads to a heavy computation load. Inspired by the idea of universal and local learning simultaneously, an adaptive local weighted kernel-based regression (ALW-KR) method is proposed. That is, adaptive weights are assigned to corresponding samples based on the similarity measurement, followed by a recursive updating to obtain local models. This ALW-KR framework is applied to the prediction of biomass concentration in the penicillin fed-batch process. The experimental results show that the proposed ALW-KR model could predict the biomass concentration more accurate and robust to batch-to-batch variation than traditional KR methods.
Starting Page	105
Ending Page	109
File Size	768808
Page Count	5
File Format	PDF
ISBN	9781424465828
e-ISBN	9781424465859
DOI	10.1109/ICICISYS.2010.5658490
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Similarity index Recursive updating Silicon Fed-batch process modeling Polymers Chemicals Kernel-based regression Adaptive local weight
Content Type	Text
Resource Type	Article

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