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

被引：0

作者：

Zhang, Xian-xia ^{[1
]}

Fu, Zhi-qiang ^{[1
]}

Shan, Wei-lu ^{[1
]}

Wang, Bing ^{[1
]}

Zou, Tao ^{[2
]}

机构：

[1] Shanghai Univ, Sch Mechatron & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200072, Peoples R China

[2] Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Peoples R China

来源：

APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING | 2016年 / 2016卷

基金：

美国国家科学基金会;

关键词：

D O I：

10.1155/2016/5241279

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 froma 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.

引用

页数：12

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