基于深层信念网络的太赫兹光谱识别

摘要

特征提取和分类是太赫兹光谱识别的关键。部分物质在太赫兹波段内没有明显的吸收峰，难以人工定义、提取特征及分类识别，为此，结合深度信念网络（deep belief network ，DBN）和K-Nearest Neighbors （KNN）分类器的优点，提出了一种基于DBN的太赫兹光谱识别方法。首先利用S-G滤波和三次样条插值对ATP ，acetylcholine-bromide ，bifenthrin ，buprofezin ，carbazole ，bleomycin ，buckminster 和 cylotriphosp-hazene在0.9～6 THz内的太赫兹透射光谱进行归一化处理；然后由两层受限波尔兹曼机（restricted Boltz-mann machine ，RBM ）构建DBN模型，并采用逐层无监督的方法训练模型，以自动提取太赫兹光谱特征；最后用KNN分类器对8种物质的太赫兹透射光谱进行分类。结果表明，使用DBN 自动提取的光谱特征， KNN分类器、BP神经网络、SOM神经网络和RBF神经网络的分类准确率达到了90％以上，且KNN分类器的识别率优于其他三种分类器；采用DBN自动提取物质的太赫兹光谱特征大大减少了工作量，在海量光谱数据识别中具有广阔的应用前景。%Feature extraction and classification are the key issues of terahertz spectroscopy identification .Because many materials have no apparent absorption peaks in the terahertz band ,it is difficult to extract theirs terahertz spectroscopy feature and identi-fy .To this end ,a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper , which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier .Firstly ,cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP ,Acetylcholine-Bromide ,Bifenthrin ,Buprofezin ,Carbazole , Bleomycin ,Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9~ 6 THz .Secondly ,the DBN model was built by two restricted Boltzmann machine (RBM ) and then trained layer by layer using unsupervised approach . Instead of using handmade features ,the DBN was employed to learn suitable features automatically with raw input data .Finally , a KNN classifier was applied to identify the terahertz spectrum .Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90% ,which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum .Furthermore ,this KNN classifier was compared with others (BP neural network ,SOM neural network and RBF neural network ) .Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers .Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction .This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy .