摘要
基于采集的电站锅炉燃烧器火焰图像,利用数字图像处理技术,讨论了特征值的意义和提取方法,提取了火焰图像特征区内灰度的平均值和标准差2个特征向量,运用现代人工神经网络智能理论,设计并改进了ART2网络算法,经过训练和实际应用后,ART2网络对一定工况的旋流燃烧器和直流燃烧器火焰燃烧状态都具有很好的识别能力,判别准确,网络稳定,实现燃烧状态实时判断,在现场取得了良好的实际应用效果。
According to the flame image which gathers from the tangential burner and the swirl burner,based on the digital image processing technology, the characteristic value significance and withdraws method are discussed, The gradation mean value and standard difference of two characteristics vectors in the flame image characteristic area are withdrawn. Applying modern artificial nerve network intelligence theory, the ART2 network algorithm is designed and improved. After the process training and the practical application, the ART2 network has the very good recognition capability to the certain operating mode eddy burner and the direct current burner flame burning condition ,and the distinction is accurate, the network is stable ,burning condition real-time judgement is realized. It is the good practical application effect in the scene.
出处
《传感器与微系统》
CSCD
北大核心
2007年第5期90-93,共4页
Transducer and Microsystem Technologies
关键词
火焰图像
燃烧诊断
人工神经网络
自适应共振理论网络
flame image
combustion diagnosis
artificial neural network
adaptive resoance theory （ART） network
作者简介
隋金雪（1977-），男，山东高密人，助教，现从事智能控制以及新型传感器的检测研究。
