Dynamic soft-sensing model by combining diagonal recurrent neural network with Levinson predictor

被引：0
|
作者：
Geng, Hui [1 ]
Xiong, Zhihua [1 ]
Mao, Shuai [1 ]
Xu, Yongmao [1 ]
机构：
[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS | 2006年 / 3973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：
Dynamic soft-sensing model of diesel oil solidifying point (DOSP) in crude distillation unit (CDU) is proposed based on diagonal recurrent neural network (DRNN). Because of long time-delay of the DOSP measurements, multi-step-ahead predictions are obtained recursively by Levinson predictor and then used as input of DRNN. Simulation results on the actual industrial process data show that the proposed dynamic soft-sensing model took good effects practically and significantly diminished the time-delay of output value.
引用
收藏
页码：1059 / 1064
页数：6
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