Affiliated with RCfalse
Status已發表Published
Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction
Han, Min1; Li, Weijie2; Feng, Shoubo2; Qiu, Tie3; Chen, C. L.Philip4,5,6
2021-06-01
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Volume32Issue:6Pages:2320-2329
Abstract

How to make full use of the evolution information of chaotic systems for time-series prediction is a difficult issue in dynamical system modeling. In this article, we propose a maximum information exploitation broad learning system (MIE-BLS) for extreme information utilization of large-scale chaotic time-series modeling. An improved leaky integrator dynamical reservoir is introduced in order to capture the linear information of chaotic systems effectively. It can not only capture the information of the current state but also achieve the compromise with historical states in the dynamical system. Furthermore, the feature is mapped to the enhancement layer by nonlinear random mapping to exploit nonlinear information. The cascading mechanism promotes the information propagation and achieves feature reactivation in dynamical modeling. Discussions about maximum information exploration and the comparisons with ResNet, DenseNet, and HighwayNet are presented in this article. Simulation results on four large-scale data sets illustrate that MIE-BLS could achieve better performance of information exploration in large-scale dynamical system modeling.

KeywordBroad Learning System (Bls) Chaotic Time Series Feature Reactivation Maximum Information Exploitation (Mie) Prediction
DOI10.1109/TNNLS.2020.3004253
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000658349600002
Scopus ID2-s2.0-85107368878
Fulltext Access
Citation statistics
Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorHan, Min
Affiliation1.Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116024, China
2.Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China
3.School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China
4.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, Macao
5.Navigation College, Dalian Maritime University, Dalian, 116026, China
6.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China
Recommended Citation
GB/T 7714
Han, Min,Li, Weijie,Feng, Shoubo,et al. Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction[J]. IEEE Transactions on Neural Networks and Learning Systems,2021,32(6):2320-2329.
APA Han, Min,Li, Weijie,Feng, Shoubo,Qiu, Tie,&Chen, C. L.Philip.(2021).Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction.IEEE Transactions on Neural Networks and Learning Systems,32(6),2320-2329.
MLA Han, Min,et al."Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction".IEEE Transactions on Neural Networks and Learning Systems 32.6(2021):2320-2329.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Han, Min]'s Articles
[Li, Weijie]'s Articles
[Feng, Shoubo]'s Articles
Baidu academic
Similar articles in Baidu academic
[Han, Min]'s Articles
[Li, Weijie]'s Articles
[Feng, Shoubo]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Han, Min]'s Articles
[Li, Weijie]'s Articles
[Feng, Shoubo]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.