期刊文献+

基于支持向量机的传感器管理技术研究

Study on sensor management based on support vector machine
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摘要 传感器的优化管理是影响多传感器目标分配问题的重要环节。在分析传感器管理基本要求的基础上,提出了一种基于支持向量机(SVM)的新型目标判定方法。利用SVM算法能够对经验风险和置信范围进行有效控制的优点,以进行多传感器系统的融合预测与可靠性检测,最终实现了在实测数据和可靠的目标预测信息下,对传感器资源的有效管理。仿真实例证明了该传感器管理方法的合理性和有效性。 Sensor optimal management is very important to multi-sensor system in target assignment. To solve the problem effectively, the basic requirement of sensor management are analyzed, and a novel target assignment algorithm is proposed based on support vector machine (SVM) , Through the active control to the experience risk and belief range using SVM, the fusion forecast and reliable examination of multi-sensor system is carried on. Finally, the effective management to sensor resources is realized under measured data and reliable target prediction information. The results of simulations indicate that the method is effective and reasonable.
出处 《传感器与微系统》 CSCD 北大核心 2008年第11期21-23,共3页 Transducer and Microsystem Technologies
基金 国家"863"计划资助项目(2006AA10A301)
关键词 传感器管理 目标判定 支持向量机 sensor management target determination support vector machine(SVM)
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参考文献9

  • 1Lu D,Zeng Y, Yao Y. Sensor management based on cross-entropy[C]// Instrumentation and Measurement Technology Conference, Proceedings of the 20th IEEE ,2003 :1555 -1557.
  • 2Hu Zhonghui, Cai Yunze, Li Yuangui,et al. Data fusion for fault diagnosis using multi-class support vector machines [ J ]. Journal of Zhejiang University Science A ,2005,6 ( 10 ) :1030 -1039.
  • 3Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods [M]. Beijing: Publishing House of Electronic Industry,2004.
  • 4Ma J S,Theiler J, Perkins S. Accurate on-line support vector regression [ J ]. Neural Computation,2003,15 ( 11 ) :2683 -2703.
  • 5Chang Y Q,Lu Z,Wang F L,et al. Soft sensing modeling based on stacked least square-support vector machine and its application[C]//The Sixth World Congress on Intelligent Control and Automation. Dalian:Dalian University of Technology Press,2006: 4846 -4850.
  • 6Zhao Shuhe. Remote sensing data fusion using support vector machine[C]//Proc of 2004 Geoscience and Remote Sensing Symposium. Anchorage ,2004:2575 -2578.
  • 7Suykens J A K. Nonlinear modeling and support vector machine[ C ]// Proceedings of the IEEE Instrumentation and Measurement Technology. New York:IEEE ,2001:287 -294.
  • 8Shilton A,Palaniswami M, Ralph D,et al. Incremental training of support vector machines[J].IEEE Transactions on Neural Networks ,2005 ( 16 ) : 114 -131.
  • 9Zhang Ling, Zhang Bo. Relationship between support vector set and kernel functions in SVM [ J ]. J Comput Sci & Technol,2002, 17(5) :549-552.

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