Fuel Identification Based on the Least Squares Support Vector Machines
Fuel Identification Based on the Least Squares Support Vector Machines
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DOI：
10.4028/www.scientific.net/amr.317-319.1237
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
Y. Huang;Shi Liu;Jie Li;Lei Jia;Z. Li
中科院分区：
文献类型：
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作者：
Y. Huang;Shi Liu;Jie Li;Lei Jia;Z. Li
燃料类型的确定对保证电厂的安全性和经济性起着重要的作用。为了获取燃烧过程中的火焰信号，设计了火焰检测系统，搭建了实验平台。本文提取了火焰信号的均值、峰值、闪烁频率和闪烁强度等参数作为火焰信号的特征量。提出了一种基于最小二乘支持向量机（LSSVM）的火焰类型识别方法。鉴定结果更为理想，正确率达100%。这表明，该方法将四个特征量与LSSVM相结合，可以获得较好的燃料类型识别效果。
The identification of the fuel types plays an important role in ensuring the safety and economics of the power plants. In order to obtain the flame signal in the process of combustion, a flame detection system is designed and a laboratorial platform is constructed. This paper extracts the signal parameters—the mean, the peak-peak value, the flicker frequency, and the flicker intensity —and takes them as the characteristic quantities of the flame signal. Based on the least squares support vector machines (LSSVM), an efficient method of identifying the flame types is developed. The result of the identification is more ideal, with the correct identification rate up to 100%. This shows that the method combined the four characteristic quantities with the LSSVM can obtain a good result in the identification of the fuel types.