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Research on Data Fusion of Adaptive Weighted Multi-Source Sensor 被引量:3

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摘要 Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.
出处 《Computers, Materials & Continua》 SCIE EI 2019年第9期1217-1231,共15页 计算机、材料和连续体(英文)
基金 This study was supported by National Key Research and Development Project(Project No.2017YFD0301506) National Social Science Foundation(Project No.71774052) Hunan Education Department Scientific Research Project(Project No.17K044 17A092).
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  • 1毛勇,周晓波,夏铮,尹征,孙优贤.特征选择算法研究综述[J].模式识别与人工智能,2007,20(2):211-218. 被引量:95
  • 2Guyon I, Elisseeff A. An introduction to variable and feature selection. The Journal of Machine Learning Research, 2003, 3:1157-1182.
  • 3Guyon I, Weston J, Barnhill S, et al. Gene selection for cancer classification using support vector machines. Machine Learning, 2002, 46(1-3): 389-422.
  • 4Rakotomamonjy A. Variable selection using svm based criteria. The Journal of Machine Learning Research, 2003, 3: 1357- 1370.
  • 5Duan K B, Rajapakse J C, Wang H, et al. Multiple SVM- RFE for gene selection in cancer classification with expression data. IEEE Transactions on NanoBioscience, 2005, 4(3): 228-234.
  • 6Xia H, Hu B Q. Feature selection using fuzzy support vector machines. Fuzzy Optimization and Decision Making, 2006, 5(2): 187-192.
  • 7Zhou X, Tuck D P. MSVM-RFE: Extensions of SVM-RFE for multiclass gene selection on DNA microarray data. Bioinformatics, 2007, 23(9): 1106-1114.
  • 8Maldonado S, Weber R. A wrapper method for feature selection using support vector machines. Information Sciences, 2009, 179(13): 2208-2217.
  • 9Somol P, Novovicova J. Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(11): 1921-1939.
  • 10Tapia E, Bulacio P, Angelone L. Sparse and stable gene selection with consensus SVM-RFE. Pattern Recognition Letters, 2012, 33(2): 164-172.

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