A hybrid soft sensor for measuring hot-rolled strip temperature in the laminar cooling process

被引：18
|
作者：
Pian, Jinxiang [1 ,2 ]
Zhu, Yunlong [2 ]
机构：
[1] Shenyang Jianzhu Univ, Sch Informat & Control Engn, Shenyang 110168, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, CAS Key Lab Networked Control Syst, Shenyang 110016, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 169卷
基金：
中国国家自然科学基金;
关键词：
Soft sensors; Laminar cooling systems; Case-based reasoning (CBR); Radial basis function (RBF) neural networks; SYSTEM; STEEL; TABLE; MILLS;
D O I：
10.1016/j.neucom.2014.09.089
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To overcome the difficulties associated with the frequently varying operating conditions of the laminar cooling process and measuring the strip temperature in the cooling process online, a soft sensor model of the hot-rolled strip is proposed which combines mathematical and hybrid intelligent methods. The proposed approach is based on computational intelligence techniques, where RBF neural networks, CBR and fuzzy logic reasoning are employed to estimate process parameters for predicting the coiling temperature of the strips. A number of simulation tests using industrial data are conducted where the desired numerical results are obtained. It has been shown that the proposed soft sensor has a high potential for being used to effectively measure the strip temperature in the laminar cooling process. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：457 / 465
页数：9
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