A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems
Joint Authors
Zhang, Xian-Xia
Fu, Zhi-qiang
Shan, Wei-lu
Zou, Tao
Wang, Bing
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-14
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Many industrial processes are inherently distributed in space and time and are called spatially distributed dynamical systems (SDDSs).
Sensor placement affects capturing the spatial distribution and then becomes crucial issue to model or control an SDDS.
In this study, a new data-driven based sensor placement method is developed.
SVR algorithm is innovatively used to extract the characteristics of spatial distribution from a spatiotemporal data set.
The support vectors learned by SVR represent the crucial spatial data structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number.
A systematic sensor placement design scheme in three steps (data collection, SVR learning, and sensor locating) is developed for an easy implementation.
Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy controlled spatially distributed systems.
American Psychological Association (APA)
Zhang, Xian-Xia& Fu, Zhi-qiang& Shan, Wei-lu& Wang, Bing& Zou, Tao. 2016. A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1094905
Modern Language Association (MLA)
Zhang, Xian-Xia…[et al.]. A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1094905
American Medical Association (AMA)
Zhang, Xian-Xia& Fu, Zhi-qiang& Shan, Wei-lu& Wang, Bing& Zou, Tao. A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1094905
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094905