Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction
文献类型：期刊论文
| 作者 | Han, Min1; Feng, Shoubo1; Chen, C. L. Philip2,3,4; Xu, Meiling1; Qiu, Tie5 |
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2019-09-01 |
| 卷号 | 31期号:9页码:1809-1821 |
| 关键词 | Manifold learning dynamical system nonuniform embedding broad learning system time series |
| ISSN号 | 1041-4347 |
| DOI | 10.1109/TKDE.2018.2866149 |
| 通讯作者 | Han, Min(minhan@dlut.edu.cn) |
| 英文摘要 | High-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. |
| WOS关键词 | ECHO STATE NETWORKS ; LAPLACIAN EIGENMAPS ; SPARSE REGRESSION |
| 资助项目 | National 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000480352800013 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27588]  |
| 专题 | 离退休人员 |
| 通讯作者 | Han, Min |
| 作者单位 | 1.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 |
| 推荐引用方式 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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