Universal Approximation Capability of Broad Learning System and Its Structural Variations

doi:10.1109/TNNLS.2018.2866622

CASIA OpenIR > 离退休人员

	Universal Approximation Capability of Broad Learning System and Its Structural Variations
	Chen, C. L. Philip 1,2; Liu, Zhulin 1; Feng, Shuang 1,3
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2019-04-01
卷号	30 期号:4 页码:1191-1204
通讯作者	Liu, Zhulin(zhulinlau@gmail.com)
摘要	After 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.
关键词	Broad learning system (BLS) deep learning face recognition functional link neural networks (FLNNs) non-linear function approximation time-variant big data modeling universal approximation
DOI	10.1109/TNNLS.2018.2866622
关键词[WOS]	REGULARIZATION ; RECOGNITION ; ALGORITHM ; NETWORKS ; MACHINE
收录类别	SCI
语种	英语
资助项目	National 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 ; National 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 ; National 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
项目资助者	National Natural Science Foundation of China ; Macau Science and Technology Development Fund ; University of Macau
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:000461854100017
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：324[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/173211/26824
专题	离退休人员
通讯作者	Liu, Zhulin
作者单位	1.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
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	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.