CASIA OpenIR
Universal Approximation Capability of Broad Learning System and Its Structural Variations
Chen, C. L. Philip1,2; Liu, Zhulin1; Feng, Shuang1,3
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
2019-04-01
Volume30Issue:4Pages:1191-1204
Corresponding AuthorLiu, Zhulin(zhulinlau@gmail.com)
AbstractAfter a very fast and efficient discriminative broad learning system (BLS) that takes advantage of flatted structure and incremental learning has been developed, here, a mathematical proof of the universal approximation property of BLS is provided. In addition, the framework of several BLS variants with their mathematical modeling is given. The variations include cascade, recurrent, and broad-deep combination structures. From the experimental results, the BLS and its variations outperform several exist learning algorithms on regression performance over function approximation, time series prediction, and face recognition databases. In addition, experiments on the extremely challenging data set, such as MS-Celeb-1M, are given. Compared with other convolutional networks, the effectiveness and efficiency of the variants of BLS are demonstrated.
KeywordBroad learning system (BLS) deep learning face recognition functional link neural networks (FLNNs) non-linear function approximation time-variant big data modeling universal approximation
DOI10.1109/TNNLS.2018.2866622
WOS KeywordREGULARIZATION ; RECOGNITION ; ALGORITHM ; NETWORKS ; MACHINE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61751202] ; National Natural Science Foundation of China[61751205] ; National Natural Science Foundation of China[61572540] ; Macau Science and Technology Development Fund[019/2015/A1] ; Macau Science and Technology Development Fund[079/2017/A2] ; Macau Science and Technology Development Fund[024/2015/AMJ] ; University of Macau
Funding OrganizationNational Natural Science Foundation of China ; Macau Science and Technology Development Fund ; University of Macau
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000461854100017
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:14[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26824
Collection中国科学院自动化研究所
Corresponding AuthorLiu, Zhulin
Affiliation1.Univ Macau, Fac Sci & Technol, Macau 99999, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
3.Beijing Normal Univ, Sch Appl Math, Zhuhai 519087, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen, C. L. Philip,Liu, Zhulin,Feng, Shuang. Universal Approximation Capability of Broad Learning System and Its Structural Variations[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(4):1191-1204.
APA Chen, C. L. Philip,Liu, Zhulin,&Feng, Shuang.(2019).Universal Approximation Capability of Broad Learning System and Its Structural Variations.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(4),1191-1204.
MLA Chen, C. L. Philip,et al."Universal Approximation Capability of Broad Learning System and Its Structural Variations".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.4(2019):1191-1204.
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