Multi-model Dynamic Fusion Soft-sensing Modeling and Its Application

被引：0
|
作者：
Lu, Chunyan [1 ,2 ]
Li, Wei [1 ,2 ]
Zhu, Chaoqun [1 ]
机构：
[1] Lanzhou Univ Technol, Lanzhou 730050, Gansu, Peoples R China
[2] Gansu Prov Key Lab Ind Proc Adv Control, Lanzhou 730050, Gansu, Peoples R China
来源：
PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年
关键词：
Soft sensor; Fuzzy c-means clustering; Radial basis function; Least square support vector machine; Gauss-Markov estimation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a multi-model dynamic fusion soft sensor modeling method based on Gauss-Markov estimation is proposed. Firstly, the fuzzy c-means algorithm is used to cluster the input samples of the model. The radial basis function and least square support vector machine are used to establish multiple sub-models for each clustering. The multi-model outputs are predicted by dynamic fusing the values of sub-models based on the Gauss-Markov estimation. The proposed method is applied to predict alumina powder flow in the process of alumina conveyor. The results indicate that the proposed method has higher predictive accuracy and better generalization capability in comparison with the other soft sensor methods.
引用
收藏
页码：9682 / 9685
页数：4
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