摘要
为了分类鉴别苦丁茶,采用竞争神经网络(CNN)和反向传播人工神经网络(BP网络)两种模式的人工神经网络(ANN)分别分析了各种苦丁茶的红外谱图。作者采用25个样本作训练集,11个样本作检验集,用两种网络进行了训练。结果表明,CNN网络和BP网络均能够有效地实现苦丁茶产地的鉴别,但CNN网络能够进一步地区分苦丁茶的级别。实验表明,CNN速度快,预测结果准确,可望用竞争神经网络(CNN)和红外光谱法结合分类鉴别苦丁茶。
In order to identify Ilex Kudingcha, two kinds of models of artificial neural networks （ANN）, i.e. competitive neural network and back propagation neural network, were used to analyze their infrared spectra. Ilex Kudingcha samples were collected by Fourier transform infrared （FTIR） spectra. Twenty five samples were gathered as a train set, and 11 samples as a test set, then their training was performed using two networks each. The results show that the identification of Ilex Kudingcha from different areas can be effectively performed with the competitive neural network and BP network, but the competitive neural network is used in the identification of different grades of Ilex Kudingcha. The results were better in training speed and accuracy with the competitive neural network. In conclusion, the competitive neural network combined with FTIR spectroscopy is a good method for the identification of Ilex Kudingcha.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2007年第7期1336-1339,共4页
Spectroscopy and Spectral Analysis
基金
山西省自然科学基金项目(2007011027)资助
关键词
苦丁茶
人工神经网络
竞争神经网络
反向传播人工神经网络
红外光谱
Ilex Kudingcha
Artificial neural networks （ANN）
Competitive neural network （CNN）
Back propagation neuralnetwork
Infrared spectra
