UM  > 科技學院  > 電腦及資訊科學系
Adaptive control of rapidly time-varying discrete-time system using initial-training-free online extreme learning machine
Gao X.H.; Wong K.I.; Wong P.K.; Vong C.M.
2016-06-19
Source PublicationNeurocomputing
ISSN18728286 09252312
Volume194Pages:117-125
Abstract

While multiple model adaptive control (MMAC) scheme provides a solution to systems with unknown and rapidly time-varying parameters, many offline samples must be obtained beforehand, and the number of models is difficult to be found if no prior knowledge is given. This paper proposes a new adaptive control strategy to handle such systems. The principle is to use a change detection mechanism to check if there is an abrupt change, and immediately train a new model if a change is detected. A novel online identification algorithm, namely initial-training-free online extreme learning machine (ITF-OELM), is also proposed to allow the model to be trained anytime without concerns on prior data. With this strategy, only one model is necessary as compared to MMAC, resulting in reduction on computational complexity and memory usage. Simulation results show that the proposed strategy is effective. Besides, although the use of forgetting factor in ITF-OELM can accelerate the convergence speed for system identification, sometimes it may lead to ill-conditioned covariance matrix in the recursively updating process. This paper shows that such issue can be solved by the change detection mechanism.

KeywordAdaptive Control Machine Learning System Identification Time-varying Discrete Systems
DOI10.1016/j.neucom.2016.01.071
URLView the original
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000376548100012
Fulltext Access
Citation statistics
Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Gao X.H.,Wong K.I.,Wong P.K.,et al. Adaptive control of rapidly time-varying discrete-time system using initial-training-free online extreme learning machine[J]. Neurocomputing,2016,194:117-125.
APA Gao X.H.,Wong K.I.,Wong P.K.,&Vong C.M..(2016).Adaptive control of rapidly time-varying discrete-time system using initial-training-free online extreme learning machine.Neurocomputing,194,117-125.
MLA Gao X.H.,et al."Adaptive control of rapidly time-varying discrete-time system using initial-training-free online extreme learning machine".Neurocomputing 194(2016):117-125.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Gao X.H.]'s Articles
[Wong K.I.]'s Articles
[Wong P.K.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Gao X.H.]'s Articles
[Wong K.I.]'s Articles
[Wong P.K.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Gao X.H.]'s Articles
[Wong K.I.]'s Articles
[Wong P.K.]'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.