Melt index predict by radial basis function network based on principal component analysis

被引：0

作者：

Liu, Xinggao ^{[1
]}

Yan, Zhengbing ^{[1
]}

机构：

[1] Zhejiang Univ, Natl Lab Ind Control Technol, Dept Control Sci & Engn, Hangzhou 310027, Peoples R China

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2006, PROCEEDINGS | 2006年 / 4224卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

index is considered important quality variable determining product specifications. Reliable prediction of melt index (MI) is crucial in quality control of practical propylene polymerization processes. In this paper, a radial basis function network (RBF) model based on principal component analysis (PCA) and genetic algorithm (GA) is developed to infer the MI of polypropylene from other process variables. Considering that the genetic algorithm need long time to converge, chaotic series are explored to get more effective computation rate. The PCA-RBF model is also developed as a basis of comparison research. Brief outlines of the modeling procedure are presented, followed by the procedures for training and validating the model. The research results confirm the effectiveness of the presented methods.

引用

页码：379 / 385

页数：7

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