摘要
分析了火焰辐射图像的炉膛温度场的测量原理,建立了针对测量对象的非线性优化数学模型,提出了一种基于BP网络的温度场测量算法。通过构造BP神经网络,输入图像样本,输出温度样本和选择合适的样本训练网络,可得出炉膛的温度场分布情况。经过实验验证,该测试算法能有效提高测量精度,具有较好的应用前景。
This paper analyzes the measurement principle of furnace temperature field based on flame radiation image ,establishes nonlinear optimization mathematical model aiming at measurement objects ,puts forward a temperature field measurement algorithm based on BP neural network ,by constructing the BP neural network , inputting image sample ,outputting temperature sample and choosing appropriate sample training network , which can draw the furnace temperature field distribution .Through experiments ,this algorithm can effectively improve the measurement accuracy and has a good application prospect .
出处
《实验技术与管理》
CAS
北大核心
2014年第2期27-30,36,共5页
Experimental Technology and Management
基金
河南省科技攻关计划项目(102102210251)
关键词
炉膛温度
BP神经网络
测量算法
furnace temperature
BP neural network
measurement algorithm
