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Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction
Han, Min1; Feng, Shoubo1; Chen, C. L. Philip2,3,4; Xu, Meiling1; Qiu, Tie5
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN1041-4347
2019-09-01
Volume31Issue:9Pages:1809-1821
Corresponding AuthorHan, Min(minhan@dlut.edu.cn)
AbstractHigh-dimensional and large-scale time series processing has aroused considerable research interests during decades. It is difficult for traditional methods to reveal the evolution state in dynamical systems and discover the relationship among variables automatically. In this paper, we propose a unified framework for nonuniform embedding, dynamical system revealing, and time series prediction, termed as Structured Manifold Broad Learning System (SM-BLS). The structured manifold learning is introduced for nonuniform embedding and unsupervised manifold learning simultaneously. Graph embedding and feature selection are both considered to depict the intrinsic structure connections between chaotic time series and its low-dimensional manifold. Compared with traditional methods, the proposed framework could discover potential deterministic evolution information of dynamical systems and make the modeling more interpretable. It provides us a homogeneous way to recover the chaotic attractor from multivariate and heterogeneous time series. Simulation analysis and results show that SM-BLS has advantages in dynamic discovery and feature extraction of large-scale chaotic time series prediction.
KeywordManifold learning dynamical system nonuniform embedding broad learning system time series
DOI10.1109/TKDE.2018.2866149
WOS KeywordECHO STATE NETWORKS ; LAPLACIAN EIGENMAPS ; SPARSE REGRESSION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61773087] ; National Natural Science Foundation of China[61672131] ; Fundamental Research Funds for the Central Universities[DUT17ZD216] ; Fundamental Research Funds for the Central Universities[DUT16QY27]
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000480352800013
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27589
Collection中国科学院自动化研究所
Corresponding AuthorHan, Min
Affiliation1.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Liaoning, Peoples R China
2.Univ Macau, Dept Comp & Informat Sci, Fac Sci & Technol, Macau 99999, Peoples R China
3.Dalian Maritime Univ, Dalian 116026, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
5.Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China
Recommended Citation
GB/T 7714
Han, Min,Feng, Shoubo,Chen, C. L. Philip,et al. Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2019,31(9):1809-1821.
APA Han, Min,Feng, Shoubo,Chen, C. L. Philip,Xu, Meiling,&Qiu, Tie.(2019).Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,31(9),1809-1821.
MLA Han, Min,et al."Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 31.9(2019):1809-1821.
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