Weighted Broad Learning System and Its Application in Nonlinear Industrial Process Modeling

doi:10.1109/TNNLS.2019.2935033

CASIA OpenIR > 离退休人员

	Weighted Broad Learning System and Its Application in Nonlinear Industrial Process Modeling
	Chu, Fei 1,2,3,4; Liang, Tao 1; Chen, C. L. Philip 5,6,7; Wang, Xuesong 1,4; Ma, Xiaoping 1
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2020-08-01
卷号	31 期号:8 页码:3017-3031
通讯作者	Wang, Xuesong(wangxuesongcumt@163.com)
摘要	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.
关键词	Learning systems Training Heuristic algorithms Neural networks Automation Process control Control engineering Broad learning system (BLS) incremental learning algorithm noise and outliers weighted penalty factor
DOI	10.1109/TNNLS.2019.2935033
关键词[WOS]	RESTRICTED BOLTZMANN MACHINE ; OUTLIER DETECTION ; ROBUST ; REGRESSION
收录类别	SCI
语种	英语
资助项目	National Nature Science Foundation of China[61973304] ; National Nature Science Foundation of China[61503384] ; National Nature Science Foundation of China[61873049] ; National Nature Science Foundation of China[61751202] ; National Nature Science Foundation of China[61751205] ; National Nature Science Foundation of China[61572540] ; National Nature Science Foundation of China[U1813203] ; National Nature Science Foundation of China[U1801262] ; Selection and Training Project of High-level Talents in the Sixteenth Six Talent Peaks of Jiangsu Province[DZXX-045] ; Open Subject of State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20190105] ; Open Fund National Engineering Research Center of Coal Preparation and Purification, China University of Mining and Technology[2018NERCCPP-B03] ; Open Foundation of State Key Laboratory of Process Automation in Mining Metallurgy ; Macau Science and Technology Development Fund (FDCT)[079/2017/A2] ; Macau Science and Technology Development Fund (FDCT)[024/2015/AMJ] ; Macau Science and Technology Development Fund (FDCT)[0119/2018/A3] ; Postgraduate Research & Practice Innovation Program of Jiangsu Province[SJCX18_0662]
项目资助者	National Nature Science Foundation of China ; Selection and Training Project of High-level Talents in the Sixteenth Six Talent Peaks of Jiangsu Province ; Open Subject of State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences ; Open Fund National Engineering Research Center of Coal Preparation and Purification, China University of Mining and Technology ; Open Foundation of State Key Laboratory of Process Automation in Mining Metallurgy ; Macau Science and Technology Development Fund (FDCT) ; Postgraduate Research & Practice Innovation Program of Jiangsu Province
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000557365700028
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：68[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/40355
专题	离退休人员
通讯作者	Wang, Xuesong
作者单位	1.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China 2.State Key Lab Proc Automat Min & Met, Beijing 100160, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 4.Xuzhou Key Lab Artificial Intelligence & Big Data, Xuzhou 221116, Jiangsu, Peoples R China 5.South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510641, Peoples R China 6.Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China 7.Univ Macau, Fac Sci & Technol, Macau 99999, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 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,2020,31(8):3017-3031.
APA	Chu, Fei,Liang, Tao,Chen, C. L. Philip,Wang, Xuesong,&Ma, Xiaoping.(2020).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(2020):3017-3031.