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Applied Computational Intelligence and Soft Computing
Volume 2016, Article ID 5241279, 12 pages
http://dx.doi.org/10.1155/2016/5241279
Research Article

A SVR Learning Based Sensor Placement Approach for Nonlinear Spatially Distributed Systems

1Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics and Automation, Shanghai University, Shanghai 200072, China
2Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

Received 29 August 2016; Accepted 27 September 2016

Academic Editor: Wu Deng

Copyright © 2016 Xian-xia Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.