中圖分類號： TN925,；TP391.4 文獻標識碼： A DOI：10.16157/j.issn.0258-7998.201073 中文引用格式： 陳燕，顧大剛,，陳亞林. 面向傳感網(wǎng)絡多源數(shù)據(jù)融合的SVM方法[J].電子技術應用,，2021，47(11)：25-28. 英文引用格式： Chen Yan,，Gu Dagang,，Chen Yalin. SVM method for multi-source data fusion of sensor networks[J]. Application of Electronic Technique，2021,，47(11)：25-28.
SVM method for multi-source data fusion of sensor networks
Chen Yan1,，Gu Dagang1，Chen Yalin2
1.School of Mathematics and Information Science，Guiyang University,，Guiyang 550002,，China； 2.School of Management Science,，Nanjing University of Finance & Economics,，Nanjing 210046，China
Abstract： The complex nonlinear separable space is composed of multi-source sensing data and its noise. Data fusion is an important method for eliminating redundant data safely, accurately and efficiently in resource-constrained sensor networks. Because of SVM generalization ability and its convex optimization, this paper focuses on the feasibility of transforming nonlinearly separable multi-source data sets into high-dimensional linear separable spaces, based on the simulation experiment. The method based on the width parameter range estimation can accurately determine the width parameter of Gaussian kernel. For the multiple classification, the stimulation experiment show, by controlling the accumulation of errors,it is more effective to ensure the classification.
Key words : data fusion,；support vector machine(SVM),；Gaussian kernel；DAG-SVMs
