Soft sensor modeling based on DE-LSSVM

被引：0

作者：

Lin, Bihua ^{[1
]}

Gu, Xingsheng ^{[1
]}

机构：

[1] Research Institute of Automation, East China University of Science and Technology, Shanghai 200237, China

来源：

Huagong Xuebao/Journal of Chemical Industry and Engineering (China) | 2008年 / 59卷 / 07期

关键词：

Evolutionary algorithms - Optimization;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Soft sensing technique is an effective method to estimate variables which are difficult to be measured on-line in industrial processes, and the core problem of soft sensing technique is construction of an appropriate mathematical model. Support vector machine (SVM) algorithm is a machine learning method based on statistical theory. Least squares support vector machine (LSSVM) is a development of the SVM, and has a faster velocity than the standard SVM. Similar to SVM, LSSVM also has the problem of parameter selection. The differential evolution (DE) method was proposed to select hyper-parameter of LSSVM. At last DE-LSSVM was presented for soft sensor modeling on testing the content of 4-carboxybenzaldehyde (4-CBA) in terephthalic acid, and the result was satisfied.

引用

页码：1681 / 1685