| SVM和BP算法在气体识别中的对比研究 |
| |
| 引用本文： | 汪丹,张亚非.SVM和BP算法在气体识别中的对比研究[J].传感技术学报,2005,18(1):201-204. |
| |
| 作者姓名： | 汪丹 张亚非 |
| |
| 作者单位： | 上海交通大学微纳米科学技术研究院,上海,200030;上海交通大学微纳米科学技术研究院,上海,200030 |
| |
| 摘 要： | 介绍了一种可以应用于气体识别领域的新的算法-支持向量基算法(SVM),并通过同常规的神经网络算法-BP算法进行实验对比,得到了:SVM算法在数据样本不含噪声时可以得到和BP算法同样好的识别效果;在数据言本含有噪声时,该算法的识别效果相对BP算法具有明显的优势.从而证明了SVM算法在气体识别领域具有良好的研究价值和应用前景.
|
| 关 键 词： | 支持向量机 气体传感器 神经网络 气体识别 BP算法 |
| 文章编号： | 1004-1699(2005)01-0201-04 |
| 修稿时间： | 2004年7月12日 |
Research of Gas Classification Based on SVM Compared with BP |
| |
| WAN Dan,ZHANG Ya-fei.Research of Gas Classification Based on SVM Compared with BP[J].Journal of Transduction Technology,2005,18(1):201-204. |
| |
| Authors: | WAN Dan ZHANG Ya-fei |
| |
| Affiliation: | Research Institute of Micro/ Nano Science and Technology , Shanghai JiaoTong University , Shanghai 200030 , C |
| |
| Abstract: | A new algorithm will be introduced which has not been paid enough attention to Support Vector Machines (SVM). Contrasting with BP algorithm which is very normal in the field of Neural Network, some useful conclusion can be gained during experiments as following: By using SVM, we can get the same impact as BP algorithm when the data do not have noises. But when the data has noises, using SVM will get better effect than using BP. The conclusion shows that SVM algorithm has well research value and applied foreground in the area of gas classification. |
| |
| Keywords: | SVM gas sensor neural network gas classification BP |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《传感技术学报》浏览原始摘要信息 |
|
点击此处可从《传感技术学报》下载全文 |
