UM
Universal Approximation Capability of Broad Learning System and Its Structural Variations
Chen,C. L.Philip1,2,3; Liu,Zhulin1; Feng,Shuang1,4
2019-04-01
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Volume30Issue:4Pages:1191-1204
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) nonlinear function approximation time-variant big data modeling universal approximation
DOI10.1109/TNNLS.2018.2866622
URLView the original
Language英语
Scopus ID2-s2.0-85053134568
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Cited Times [WOS]:63   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorLiu,Zhulin
Affiliation1.Faculty of Science and Technology,University of Macau,99999,Macao
2.College of Navigation,Dalian Maritime University,Dalian,116026,China
3.State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing,100080,China
4.School of Applied Mathematics,Beijing Normal University,Zhuhai,519087,China
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
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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