SIA OpenIR  > 数字工厂研究室
ELM-PSO-FCM based missing values imputation for byproduct gas flow data analysis
Sun XY(孙雪莹)1,2,3; Wang Z(王卓)1,2,3; Hu JT(胡静涛)1,2,3
Department数字工厂研究室
Conference Name3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
Conference DateMarch 15-17, 2019
Conference PlaceChengdu, China
Author of SourceChengdu Global Union Academy of Science and Technology ; Chongqing Geeks Education Technology Co., Ltd ; Chongqing Global Union Academy of Science and Technology ; Global Union Academy of Science and Technology ; IEEE Beijing Section
Source PublicationProceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019
PublisherIEEE
Publication PlaceNew York
2019
Pages56-59
Indexed ByEI ; CPCI(ISTP)
EI Accession number20192607109705
WOS IDWOS:000491352900012
Contribution Rank1
ISBN978-1-5386-6243-4
KeywordMissing data Imputation Fuzzy c-means PSO Extreme learning machine
AbstractByproduct gas flow data analysis is necessary for scheduling optimization in iron and steel enterprises. However, missing values are inevitable for reasons like sensor fault and data transmission error. Our work focused on data loss problem and proposed a robust method for missing data imputation. Fuzzy c-means (FCM) was employed as the basic principle in our work. In order to improve the robustness of FCM, three strategies were introduced to the approach. Linear interpolation was first adopted to enhance the accuracy of convergence. Parameters of FCM were also optimized by means of Particle Swarm optimization (PSO). Furthermore, Extreme Learning Machine (ELM) was used to improve the generalization performance of the data imputation model. To fully evaluate the proposed method, several experiments were elaborated and the results proved the superior characteristics.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/25234
Collection数字工厂研究室
Corresponding AuthorSun XY(孙雪莹)
Affiliation1.Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing
4.100049, China
Recommended Citation
GB/T 7714
Sun XY,Wang Z,Hu JT. ELM-PSO-FCM based missing values imputation for byproduct gas flow data analysis[C]//Chengdu Global Union Academy of Science and Technology, Chongqing Geeks Education Technology Co., Ltd, Chongqing Global Union Academy of Science and Technology, Global Union Academy of Science and Technology, IEEE Beijing Section. New York:IEEE,2019:56-59.
Files in This Item: Download All
File Name/Size DocType Version Access License
ELM-PSO-FCM based mi(173KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sun XY(孙雪莹)]'s Articles
[Wang Z(王卓)]'s Articles
[Hu JT(胡静涛)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun XY(孙雪莹)]'s Articles
[Wang Z(王卓)]'s Articles
[Hu JT(胡静涛)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun XY(孙雪莹)]'s Articles
[Wang Z(王卓)]'s Articles
[Hu JT(胡静涛)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: ELM-PSO-FCM based missing values imputation for byproduct gas flow data analysis.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.