Soft Sensor Modeling Based on Multiple Extreme Learning Machine

被引：0

作者：

Wang, Gaitang ^{[1
]}

Li, Ping ^{[2
]}

Su, Chengli ^{[2
]}

机构：

[1] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China

[2] Liaoning Shihua Univ, Sch Informat & Control Engn, Fushun, Peoples R China

来源：

MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8 | 2012年 / 433-440卷

关键词：

multiple extreme learning machine(MELM); Gaussian process(GP); extreme learning machine(ELM); soft sensor model; adaptive affinity propagation(AAP);

D O I：

10.4028/www.scientific.net/AMR.433-440.3003

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Presented is a multiple model soft sensing method based on extreme learning machine (MELM) algorithm, to solve the problem that single ELM model has lower predictive precision and over-fitting problems. The method adopts Gaussian process to choose secondary variable for soft sensor model. Then, samples data are divided into several groups of data by adaptive affinity propagation clustering, and the sub-models are estimated by ELM regression method. Finally, ELM is regarded as output synthesizer of sub-models. The proposed method has been applied to predict the end point of crude gasoline in delayed coking unit. Compared with single ELM modeling, the simulation results show that the algorithm has better predictive precision and good generalization performance.

引用

页码：3003 / +

页数：2

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