典型文献
Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine
文献摘要：
In recent years,a laser-induced breakdown spectrometer(LIBS)combined with machine learning has been widely developed for steel classification.However,the much redundant information of LIBS spectra increases the computation complexity for classification.In this work,restricted Boltzmann machines(RBM)and principal component analysis(PCA)were used for dimension reduction of datasets,respectively.Then,a support vector machine(SVM)was adopted to process feature information.Two models(RBM-SVM and PCA-SVM)are compared in terms of performance.After optimization,the accuracy of the RBM-SVM model can achieve 100％,and the maximum dimension reduction time is 33.18 s,which is nearly half of that of the PCA model(53.19 s).These results preliminarily indicate that LIBS combined with RBM-SVM has great potential in the real-time classification of steel.
文献关键词：
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作者姓名：
Qingdong ZENG;Guanghui CHEN;Wenxin LI;Zitao LI;Juhong TONG;Mengtian YUAN;Boyun WANG;Honghua MA;Yang LIU;Lianbo GUO;Huaqing YU
作者机构：
School of Physics and Electronic-information Engineering,Hubei Engineering University,Xiaogan 432000,People's Republic of China;Wuhan National Laboratory for Optoelectronics(WNLO),Huazhong University of Science and Technology,Wuhan 430074,People's Republic of China;Faculty of Physics and Electronic Science,Hubei University,Wuhan 430062,People's Republic of China
文献出处：
引用格式：
[1]Qingdong ZENG;Guanghui CHEN;Wenxin LI;Zitao LI;Juhong TONG;Mengtian YUAN;Boyun WANG;Honghua MA;Yang LIU;Lianbo GUO;Huaqing YU-.Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine)[J].等离子体科学和技术（英文版）,2022(08):71-76
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AB值：
0.562649
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