Product Quality Prediction by a Neural Soft-Sensor Based on MSA and PCA

A novel soft-sensor model which incorporates PCA （principal component analysis）, RBF （Radial Basis Function） networks, and MSA （Multi-scale analysis）, is proposed to infer the properties of manufactured products from real process variables. PCA is carried out to select the most relevant process feat...

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Bibliographic Details
Published in:International journal of automation and computing 2006, Vol.3 (1), p.17-22
Main Authors: Shi, Jian, Liu, Xing-Gao
Format: Article
Language:English
Subjects:
Studies
主成分分析
产品质量
径向基函数网络
神经系统软传感器
通用换算分析
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Summary:A novel soft-sensor model which incorporates PCA （principal component analysis）, RBF （Radial Basis Function） networks, and MSA （Multi-scale analysis）, is proposed to infer the properties of manufactured products from real process variables. PCA is carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis is introduced to acquire much more information and to reduce uncertainty in the system; and RBF networks are used to characterize the nonlinearity of the process. A prediction of the melt index （MI）, or quality of polypropylene produced in an actual industrial process, is taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy.
ISSN:1476-8186
1751-8520
DOI:10.1007/s11633-006-0017-9