a svr learning based sensor placement approach for nonlinear spatially distributed systems

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ID: 165426
2016
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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.
Reference Key
zhang2016applieda Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xian-xia Zhang;Zhi-qiang Fu;Wei-lu Shan;Bing Wang;Tao Zou
Journal journal of evidence-based complementary & alternative medicine
Year 2016
DOI
10.1155/2016/5241279
URL
http://dx.doi.org/10.1155/2016/5241279
Keywords
soil erosion computer science electronic computers

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