中文摘要：

介绍和分析了人工放射性气溶胶在线监测仪氡子体扣除算法中比例系数扣除法,现有算法存在分类粗糙、扣除准确度不高以及适应性不强等不足。为进一步提高扣除的准确度,降低检测限,提出了利用聚类分析先对谱线进行分类,然后在每个类中利用神经网络进行计算,最后进行扣除的方法。测试结果证明了聚类分析和神经网络扣除方法均能明显降低人工放射性气溶胶在线监测仪的检测限。

英文摘要：

Background： The proportion subtraction method used in radon daughters subtraction algorithm for continuous artificial radioactive aerosol monitor has disadvantages such as rough classfication, less accuracy and low adaptability. Purpose： This study aims to improve the accuracy of subtraction to reduce the detection limit. Methods：A novel algorithm is proposed by classifying the spectral lines through clustering analysis and then calculating each clustering using neural network. Experimental verifcation is performed to compare this method with the proportion subtraction method. Results： The results showed that the cluster analysis and neural network subtraction algorithm can reduce more than 20% of the detection limit for the continuous artificial radioactive aerosol monitor. Conclusion：The algorithm proposed in this paper is effective for subtracting radon daughters.