关键词: BP神经网络, 广义回归神经网络, 主成分分析, 舌诊, 近红外光谱

Abstract: In order to objectively reflect the information carried by spectral data of tongue diagnosis of Traditional Chinese Medicine（TCM），near infrared spectroscopy and neural networks are firstly adopted to diagnose the disease.Three models，BP neural network，GRNN and PCA-GRNN，are employed for analysis and prediction of tongue spectral.Firstly the three modeling methods are analyzed，then spectral data from the healthy and diabetic patients are collected to correct tongue spectral model.Two kinds of samples including 39 cases of tongue spectrum respectively，a total of 78 samples，are to be divided into training set of 60 samples and prediction set of 18 in the neural network learning，and they are respectively trained and predicted under the three models.The result is that the smallest mean absolute error of PCA-GRNN of the three models is 13.2%，a minimum of training time 0.072 255 s，and in the condition that relative deviation is less than 0.5，the correct rate is 100%.It shows that the PCA-GRNN model can be applied to the analysis of tongue spectrometry and obtains better results，which is helpful for objectiveness of tongue diagnosis of TCM.

Key words: BP neural networks, general regression neural network, principal component analysis, tongue diagnosis, near infrared spectroscopy