研究了人工神经网络在激光诱导击穿光谱(LIBS)塑料分类识别方面的应用.选用七种常见的塑料作为实验样品,获得每种样品的170组LIBS光谱数据,利用主成分分析获得前五个主成分的得分矩阵.用每种塑料样品的130组光谱数据的主成分得分矩阵作为训练集,建立反向传播(BP)人工神经网络模型.将其余40组主成分得分作为测试数据输入训练好的模型进行分类识别,其识别准确度达到97.5％.实验结果表明,通过采用主成分分析与BP人工神经网络相结合的方法,可以很好地进行塑料激光诱导击穿光谱的分类识别,对塑料的回收利用有重要价值.%The classification of seven kinds of plastic(ABS, PET, PP, PS, PVC, HDPE and PMMA) with the laser-induced breakdown spectroscopy based on artificial neural network model was investigated in the present paper. One hundred seventy LIBS spectra for each type of plastic were collected. Firstly, all 1 190 plastics LIBS spectra were studied with principal component analysis. The first five principal components (PC) totally explain 78. A% of the original spectrum information. Therefore, the scores of five PCs of 130 LIBS spectra for each kind of plastic were chosen as the training set to build a back-propagation artificial network model. And the other 40 LIBS spectra of each sample were used as the testing set for the trained model. The classification accuracy was 97. 5%. Experimental results demonstrate that plastics can be classified by using principal component analysis and artificial neural network (BP) method.
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