Residential College | false |
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 Publication | IEEE Transactions on Neural Networks and Learning Systems |
ISSN | 2162-237X |
Volume | 31Issue: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. |
Keyword | Broad Learning System (Bls) Incremental Learning Algorithm Noise And Outliers Weighted Penalty Factor |
DOI | 10.1109/TNNLS.2019.2935033 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Computer Science ; Engineering |
WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS ID | WOS:000557365700028 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Scopus ID | 2-s2.0-85089129984 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE Faculty of Science and Technology |
Corresponding Author | Wang, Xuesong |
Affiliation | 1.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. |
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