Residential Collegefalse
Status已發表Published
Weighted broad learning system and its application in nonlinear industrial process modeling
Chu, Fei1,2,3,4; Liang, Tao1; Chen, C. L.Philip5,6,7; Wang, Xuesong1,4; Ma, Xiaoping1
2019-09-11
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Volume31Issue:8Pages:3017-3031
Abstract

Broad learning system (BLS) is a novel neural network with effective and efficient learning ability. BLS has attracted increasing attention from many scholars owing to its excellent performance. This article proposes a weighted BLS (WBLS) based on BLS to tackle the noise and outliers in an industrial process. WBLS provides a unified framework for easily using different methods of calculating the weighted penalty factor. Using the weighted penalty factor to constrain the contribution of each sample to modeling, the normal and abnormal samples were allocated higher and lower weights to increase and decrease their contributions, respectively. Hence, the WBLS can eliminate the bad effect of noise and outliers on the modeling. The weighted ridge regression algorithm is used to compute the algorithm solution. Weighted incremental learning algorithms are also developed using the weighted penalty factor to tackle the noise and outliers in the additional samples and quickly increase nodes or samples without retraining. The proposed weighted incremental learning algorithms provide a unified framework for using different methods of computing weights. We test the feasibility of the proposed algorithms on some public data sets and a real-world application. Experiment results show that our method has better generalization and robustness.

KeywordBroad Learning System (Bls) Incremental Learning Algorithm Noise And Outliers Weighted Penalty Factor
DOI10.1109/TNNLS.2019.2935033
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000557365700028
PublisherInstitute of Electrical and Electronics Engineers Inc.
Scopus ID2-s2.0-85089129984
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Faculty of Science and Technology
Corresponding AuthorWang, Xuesong
Affiliation1.School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
2.State Key Laboratory of Process Automation in Mining and Metallurgy, Beijing, 100160, China
3.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China
4.Xuzhou Key Laboratory of Artificial Intelligence and Big Data, Xuzhou, 221116, China
5.School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, China
6.Navigation College, Dalian Maritime University, Dalian, 116026, China
7.Faculty of Science and Technology, University of Macau, 99999, Macao
Recommended Citation
GB/T 7714
Chu, Fei,Liang, Tao,Chen, C. L.Philip,et al. Weighted broad learning system and its application in nonlinear industrial process modeling[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 31(8), 3017-3031.
APA Chu, Fei., Liang, Tao., Chen, C. L.Philip., Wang, Xuesong., & Ma, Xiaoping (2019). Weighted broad learning system and its application in nonlinear industrial process modeling. IEEE Transactions on Neural Networks and Learning Systems, 31(8), 3017-3031.
MLA Chu, Fei,et al."Weighted broad learning system and its application in nonlinear industrial process modeling".IEEE Transactions on Neural Networks and Learning Systems 31.8(2019):3017-3031.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chu, Fei]'s Articles
[Liang, Tao]'s Articles
[Chen, C. L.Philip]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chu, Fei]'s Articles
[Liang, Tao]'s Articles
[Chen, C. L.Philip]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chu, Fei]'s Articles
[Liang, Tao]'s Articles
[Chen, C. L.Philip]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

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