摘要
在目标发射率未知的情况下,建立一种基于RBF(radial basis function)神经网络的红外测温方法。首先推导出目标温度同辐射亮度峰值及其波长之间的强非线性关系,明确神经网络输入变量;然后基于RBF网络对样本数据进行充分学习,建立目标辐射测温模型,该模型不需要发射率输入。利用黑体和钢板目标分别作为测试目标源,验证这种方法,得到黑体测温最大相对误差为0.016%、钢板的最大相对误差为1.08%,验证了本文测温方法的合理性。
An infrared temperature-measurement method based on a radial basis function(RBF)neural network is established in the case of unknown target emissivity.First,the strong nonlinear relationship between the target temperature and the peak of the radiance curve and its wavelength is derived.The inputs to the neural network are determined.Then,according to the RBF network,sample data are studied,and a target radiation-temperature-measurement model is established.The model does not require emissivity.A blackbody and steel plate target are used as test targets to prove the proposed method.The maximum relative error of the temperature of the blackbody is 0.016%and that of the steel plate is 1.08%.These results verify the rationality of the established temperature-measurement method.
作者
席剑辉
姜瀚
XI Jianhui;JIANG Han(School of Automation,Shenyang Aerospace University,Shenyang 110136,China)
出处
《红外技术》
CSCD
北大核心
2020年第10期963-968,共6页
Infrared Technology
基金
国家自然科学基金青年基金资助项目(61503256)
辽宁省自然科学基金项目(2015020069,2015020061)
沈阳市科技创新团队项目(src201204)。
