Author/Authors :
Zhang, Xian-xia Shanghai Key Laboratory of Power Station Automation Technology - School of Mechatronics and Automation - Shanghai University, Shanghai, China , Fu, Zhi-qiang Shanghai Key Laboratory of Power Station Automation Technology - School of Mechatronics and Automation - Shanghai University, Shanghai, China , Shan, Wei-lu Shanghai Key Laboratory of Power Station Automation Technology - School of Mechatronics and Automation - Shanghai University, Shanghai, China , Wang, Bing Shanghai Key Laboratory of Power Station Automation Technology - School of Mechatronics and Automation - Shanghai University, Shanghai, China , Zou, Tao Shenyang Institute of Automation - Chinese Academy of Sciences, Shenyang, China
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 thecharacteristics of spatial distribution from a spatiotemporal data set. The support vectors learned by SVR represent the crucial spatialdata structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number. Asystematic sensor placement design scheme in three steps (data collection, SVR learning, and sensor locating) is developed for aneasy implementation. Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzycontrolled spatially distributed systems
Keywords :
Nonlinear Spatially Distributed Systems , patially distributed dynamical systems(SDDSs , Sensor Placement , SVR Learning
