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中文摘要：
针对多参数影响长距离α测量（Long range alpha detector，LRAD）系统准确度的问题，通过模拟现场管道，开展管道内LRAD敞开式测量关键参数特征研究试验，获得影响系统收集离子的特征。针对系统输入输出的非线性关系，用BP神经网络对多参数影响下系统的输出进行网络训练与预测，预测平均百分误差在5％以内。研究表明，满足放射性测量统计涨落规律条件下，BP网络对LRAD分析结果预测有较好的准确度，基本克服了系统非线性的影响。
英文摘要：
Background： The accuracy for multi-parameter of long range alpha detector （LRAD） system needs to be improved. Purpose： To overcome the nonlinear effect on accuracy and have the measurements corrected. Methods： Key-parameter characteristics that affect open LRAD monitoring inside on-site pipes were analyzed. Based on the 352 groups of data from key-parameter characteristics analyzing, this paper adopted BP neural network to train and predict the measurements. Results： The predicted result has a good consistence with measurement, and the mean percentage error of the result is less than 5%. Conclusions： Using BP to predict the analysis result of LRAD has a good accurate, and besides, overcomes the impact of the system of nonlinear basically on condition of satisfying the radioactivity measurements of the law of statistical fluctuations.
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