NDLI: Soft sensor based on least square support vector machine with limited memory for crystal particle size prediction in PTA purification process

Content Provider	IEEE Xplore Digital Library
Author	Ruilan Liu Yang Xu
Copyright Year	2008
Description	Author affiliation: Coll. of Autom., Nanjing Univ. of Posts & Telecommun., Nanjing (Ruilan Liu)
Abstract	The accurate estimation of the average crystal particle size in PTA purification process is of fundamental importance in process monitoring, advanced control and optimization. A method of least square support vector machine with limited memory is applied to model soft senor to predict the average particle size on-line. Compared with traditional least square support vector machine, Small samples in fixed length are used to train a drifting model in small scale which need not optimize its structure by pruning algorithm. Some useful information would be lost if old samples are discarded directly so an idea is proposed to introduce the information to the model by the maximum values and minimum values of overall samples... The results of simulation show that the soft sensor based on the proposed method has high precision and is suitable for time-varying system with samples which distribution is not uniform.
Starting Page	2678
Ending Page	2681
File Size	225768
Page Count	4
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4594479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Process control Artificial neural networks Crystals Automation Monitoring Educational institutions crystal particle size Soft sensor Least square support vector machine partial least square RBF neural network
Content Type	Text
Resource Type	Article

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