Low recycling and reuse rate of plastic products cause environmental pollution and resource waste.Therefore,accurate classification of waste plastics is the key to improving plastic recycling.In this paper,laser induced breakdown spectroscopy(LIBS)technology combined with genetic algorithm optimization error back propagation neural network(GA BP NN)and support vector machine(GA SVM)are used to classify and identify 10 common plastics.The plastic samples are processed by LIBS technology,and 100 sets of spectra of each plastic sample are collected.After filtering and normalizing the collected original spectrum,14 main spectral in the spectrum are extracted as characteristic spectral,and GA BP neural network and GA SVM models are established respectively.The experimental results show that the GA BP neural network model is better than the GA SVM model in identifying plastics.The GA BP neural network has a recognition accuracy of 99.25%,because GA SVM uses the ascending algorithm to classify the data set.In the case of a large number of plastic samples,the classification effect is not as good as the GA BP neural network.Therefore,the use of LIBS technology combined with different recognition algorithms can realize the classification of a variety of plastic samples,and also provide research ideas for the classification and recognition of plastic samples by different algorithms.
参考文献
相似文献
引证文献
引用本文
路永华. LIBS结合GA BP神经网络与GA SVM对塑料分类研究[J].激光与红外,2022,52(2):273~279 LU Yong-hua. Plastic classification research by LIBS combined with GA BP NN and GA SVM[J]. LASER & INFRARED,2022,52(2):273~279