神经网络在DMMP红外光谱判别中的应用

介绍了神经网络在化学毒剂红外遥感监测领域应用的概况，探讨了反向传播人工神经网络分类器应用于红外光谱鉴别的可能性。用一个甲基膦酸二甲酯红外光谱数据样本集进行了实际的训练和鉴别性能预测。训练结果表明，这种分类器在一定条件下可以将959／5以上的样本正确分离；预测结果表明，经过适当训练的神经网络分类器可以获得70％以上的鉴别率，具备了一定的识别能力。

Application of neural network in discrimination of DMMP infrared spectra

The application of the neural network to remote sensing infrared chemical detection is introduced. The possibility that the back-propagation artificial neural network (BP-ANN) classifier is implemented to discriminate infrared spectra is approached. A data set of DMMP (dimethyl methylphosphonate) infrared spectra is used to perform the actual training and discrimination performence prediction of this classifier. The training results show that 95% samples in the data set can be correctly classified under certain conditions. The prediction results show that the neural network classifier which has been properly trained has possessed relatively high discrimination ability, and the discrimination rates more than 70% can be obtained from such classifiers.