The Recognition of Nonlinear Fluorescence Spectra Based on a Support Vector Machine Networks

A support vector machine(SVM) network is applied to recognize the nonlinear fluorescence spectra of atmospheric impurity gases. As the number of spectrum channels is quite large, a wavelet transform (WT) is firstly adopted to remove the noises and to reduce the dimension of the data, afterward a principal component analysis (PCA) is used to extract the feature information. As result, the dimension of data is compressed from 3979 to 514(WT) and finally to 9(PCA), while the features of original nonlinear fluorescence spectra are remained. The results show that the correct recognition rate for the training samples and the testing ones has both reached 100%. Accordingly, the proposed method is efficient in inspecting of the atmospheric impurity gases in real time.