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.
基金
supported by National Natural Science Foundation of China(No.61705064)
the Natural Science Foundation of Hubei Province(No.2021CFB607)
the Natural Science Foundation of Xiaogan City(No.XGKJ2021010003)
the Project of the Hubei Provincial Department of Education(No.T201617)。
