| 结合主成分分析法的可拓神经网络三维荧光光谱的分类识别研究 |
| |
| 引用本文： | 尚云鹏,金翠云,王颖.结合主成分分析法的可拓神经网络三维荧光光谱的分类识别研究[J].北京化工大学学报(自然科学版),2013,40(5):100-103. |
| |
| 作者姓名： | 尚云鹏 金翠云 王颖 |
| |
| 作者单位： | 北京化工大学信息科学与技术学院,北京,100029;北京化工大学信息科学与技术学院,北京,100029;北京化工大学信息科学与技术学院,北京,100029 |
| |
| 摘 要： | 提出了结合主成分分析法(PCA)的可拓神经网络算法，并且将其应用于柴油、煤油、汽油的三维荧光光谱分类识别中。实验结果表明，相比传统的BP神经网络算法，该算法迭代数下降了80步，识别率由89%提高到93%，体现了结合算法的高识别率和高效性。
|
| 关 键 词： | 可拓神经网络 主成分分析 三维荧光光谱 |
| 收稿时间： | 2012-12-13 |
Three-dimensional fluorescence spectral classification based on extention neural networks combined with principal component analysis |
| |
| SHANG YunPeng
,
JIN CuiYun
,
WANG Ying.Three-dimensional fluorescence spectral classification based on extention neural networks combined with principal component analysis[J].Journal of Beijing University of Chemical Technology,2013,40(5):100-103. |
| |
| Authors: | SHANG YunPeng JIN CuiYun WANG Ying |
| |
| Institution: | College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China |
| |
| Abstract: | A combination of principal component analysis (PCA) and extension neural network algorithms has been employed for the classification and recognition of the three dimensional fluorescence spectra of diesel, kerosene and gasoline. Compared to the traditional BP neural network algorithm, there was a decrease of 80 in the number of iterations of the algorithm required and the recognition ratio increased from 89% to 93%, showing the high recognition ratio and efficiency of the combined algorithm. |
| |
| Keywords: | extension neural network principal component analysis three-dimensional fluorescence spectroscopy |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《北京化工大学学报(自然科学版)》浏览原始摘要信息 |
| 点击此处可从《北京化工大学学报(自然科学版)》下载免费的PDF全文 |
