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首页> 外文期刊>Journal of Chemical Information and Computer Sciences >A NEURAL NETWORK APPROACH TO THE DETECTION OF NUCLEAR MATERIAL LOSSES
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A NEURAL NETWORK APPROACH TO THE DETECTION OF NUCLEAR MATERIAL LOSSES

机译：核材料损耗检测的神经网络方法

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A series of repeated nuclear material balances forms a time series of often autocorrelated observations. Outliers, deviations from an in-control production process or time series pattern, indicate an out-of-control situation relative to the process norm. In this paper various methods, especially neural networks, will be examined with respect to their use to detect nuclear material diversions or losses more rapidly and accurately than currently used methods. The neural network technique will be enhanced with the use of a simulation computer program for creating the training data set. This simulation approach provides the opportunity of including outliers of various types in a data set for training the neural network because an actual process data set used for training possibly may not have outliers. In this paper, the methods will be compared on their ability to identify outliers and reduce false alarms. These methods were tested on data sets of nuclear material balances with known removals, and the results are tabulated and described, Based on these results, we believe the algorithms used will assist the nuclear industry in process control provide a new approach to nuclear material safeguards, and also provide a new approach to training neural networks for process control applications. [References: 44]
机译：一系列重复的核物质平衡形成了经常自相关观测的时间序列。离群值（偏离控制中的生产过程或时间序列模式）表示相对于过程规范的失控情况。在本文中，将对各种方法（尤其是神经网络）进行检查，以比目前使用的方法更快速，准确地检测核材料的转移或损失。将通过使用模拟计算机程序来创建训练数据集来增强神经网络技术。这种仿真方法提供了将各种类型的异常值包括在用于训练神经网络的数据集中的机会，因为用于训练的实际过程数据集可能没有异常值。在本文中，将比较这些方法在识别异常值和减少错误警报方面的能力。这些方法在已知清除量的核材料天平数据集上进行了测试，并将结果制成表格并加以描述。基于这些结果，我们认为所使用的算法将有助于核工业的过程控制，为核材料保障提供新的方法，并且还提供了一种新的方法来训练过程控制应用的神经网络。 [参考：44]

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