摘要
为了快速精确地在线测量钢水温度,采用红外CCD相机测温技术来测量钢水表面温度。利用红外CCD相机采集钢水在不同温度下的图像,计算出图像中与热电偶测温相近位置点区域的灰度均值,引入黄金分割寻优法对广义回归神经网络中扩展系数的确定进行了改进,并用传统的最小二乘法与改进的广义回归神经网络方法对灰度比和温度之间的非线性关系拟合曲线进行对比。结果表明,用改进的广义回归神经网络建立的测温模型有效地提高了在线温度测量精度,使钢水温度测量误差控制在0.1%范围内,符合工业设计要求。该研究为广义回归神经网络应用在钢水测温领域提供了参考。
In order to quickly and accurately measure the molten steel temperature on line, infrared CCD camera temperature measurement technology was used to measure the surface temperature of molten steel. The image of molten steel at different temperatures was collected by an infrared CCD camera to calculate the average of the grayscale values in the region of the image near the temperature measured by the thermocouple. The golden section optimization method was introduced to determine the expansion coefficient in the generalized regression neural network. The nonlinear curve fitting between gray scale and temperature was compared by using the traditional least square method and the modified generalized regression neural network.The results show that the temperature measurement model established by the improved generalized regression neural network can effectively improve the on-line temperature measurement accuracy and make the measurement error of molten steel temperature within 0. 1%. It meets the requirements of industrial design. This study provides a reference for the application of generalized regression neural network in the field of molten steel temperature measurement.
作者
杨友良
刘爱旭
马翠红
连畅
YANG Youliang;LIU Aixu;MA Cuihong;LIAN Chang(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China)
出处
《激光技术》
CAS
CSCD
北大核心
2018年第4期562-566,共5页
Laser Technology
基金
国家自然科学基金资助项目(61171058)
关键词
测量与计量
广义回归神经网络测温
精度提高
灰度比值
最小二乘法
measurenlent and metrology
temperature measurenmnt based on generalized regression neural network
precision improvenlent
grayscale ratio
least square method
