激光诱导击穿光谱技术结合PCA-PSO-SVM对矿石分类识别

Classification of Ores Using Laser-Induced Breakdown Spectroscopy Combined with PCA-PSO-SVM

利用激光诱导击穿光谱(LIBS)技术结合主成分分析-粒子群优化-支持向量机(PCA-PSO-SVM)算法对12类矿石进行分类识别。运用Savitzky Golay滤波、分段特征值提取法对光谱进行平滑滤波与基线校正预处理。选取经主成分分析(PCA)降维后的前25个主成分作为PSO-SVM分类模型的输入,得到对12类矿石的最佳识别准确率为100%。分别建立主成分-线性判别分析(PCA-LDA)、主成分-粒子群优化-误差反向传播神经网络(PCAPSO-BP)两种分类模型,并与PCA-PSO-SVM模型进行对比实验,结果表明,PCA-PSO-SVM分类模型的识别准确度最高,其平均识别准确率可达99.90%。

Twelve types of ores were identified using laser-induced breakdown spectroscopy combined with the principal component analysis-particle swarm optimization-support vector machine(PCA-PSO-SVM)algorithm.A Savitzky-Golay filter was used to smooth the spectrum,and the segmented eigenvalue extraction method was used to perform baseline correction on the spectrum.The first 25 principal components reduced by PCA were selected as the input to the PSO-SVM classification model,and the best recognition accuracy rate for the 12 types of ore was 100%.The PCA-PSO-SVM model was compared with two classification models,i.e.,principal component-linear discriminant analysis and a PCA-particle swarm optimization-error back propagation neural network.Experimental results showed that the recognition accuracy of the PCA-PSO-SVM classification model was the highest with an average recognition accuracy rate of up to 99.90%.