| 基于径向基函数网络的浮游植物活体三维荧光光谱分类 |
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| 引用本文： | 马伟华,刘珑龙,张建民. 基于径向基函数网络的浮游植物活体三维荧光光谱分类[J]. 计算机辅助工程, 2006, 15(3): 66-68,71 |
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| 作者姓名： | 马伟华 刘珑龙 张建民 |
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| 作者单位： | 中国海洋大学,数学系,山东,青岛,266071;中国海洋大学,数学系,山东,青岛,266071;中国海洋大学,数学系,山东,青岛,266071 |
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| 摘 要： | 将小波变换与神经网络相结合，对浮游植物活体的三维荧光光谱进行分类．首先利用小波变换对数据进行压缩，然后利用径向基函数（Radial Basis Function，RBF）神经网络对光谱曲线进行逼近，从而进行物种的识别，平均识别率高达95．8％．结果表明，该方法较传统的统计方法更方便、准确率更高．
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| 关 键 词： | 小波变换 神经网络 径向基函数 高斯函数 |
| 文章编号： | 1006-0871（2006）03-0066-03 |
| 收稿时间： | 2006-04-01 |
| 修稿时间： | 2006-04-01 |
3D fluorescence spectra classification of phytoplankton based on radial basis function networks |
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| MA Weihua,LIU Longlong,ZHANG Jianmin. 3D fluorescence spectra classification of phytoplankton based on radial basis function networks[J]. Computer Aided Engineering, 2006, 15(3): 66-68,71 |
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| Authors: | MA Weihua LIU Longlong ZHANG Jianmin |
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| Affiliation: | Dept. of Mathematics, Ocean Univ. of China, Qingdao Shandong 266071, China |
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| Abstract: | The 3D fluorescence spectra of phytoplankton is classified by wavelet transform with neural work combined. The original data is compressed by wavelet transformation method, and the curves of spectra are approximated by radial basis function(RBF) network. Thereby the species are recognized. And the average recognition rate is as high as 95.8%. Compared with the traditional statistic method, the experiments indicate that the method has good performance with high accuracy and convenience. |
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| Keywords: | wavelet transform neural network radial basis function(RBF) Gauss function |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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