To effectively monitor the variety of distributions of neutron flux, fuel power or temperatures in the reactor core, usually the ex-core and in-core neutron detectors are employed. The thermocouples for temperature measurement are installed in the coolant inlet or outlet of the respective fuel assemblies. It is necessary to reconstruct the measurement information of the whole reactor position. However, the reading of different types of detector in the core reflects different aspects of the 3D power distribution. The feasibility of reconstruction the core three-dimension power distribution by using different combinations of in-core, ex-core and thermocouples detectors is analyzed in this paper to synthesize the useful information of various detectors. A comparison of multilayer perceptron (MLP) network and radial basis function (RBF) network is performed. RBF results are more extreme precision but also more sensitivity to detector failure and uncertainty, compare to MLP networks. This is because that localized neural network could offer conservative regression in RBF. Adding random disturbance in training dataset is helpful to reduce the influence of detector failure and uncertainty. Some convolution neural networks seem to be helpful to get more accurate results by use more spatial layout information, though relative researches are still under way.

为有效监测反应堆堆芯内中子通量、燃料功率或温度的分布变化，通常采用堆外和堆内中子探测器。用于温度测量的热电偶安装在各个燃料组件的冷却剂入口或出口处。需要重建整个反应堆位置的测量信息。然而，核心中不同类型探测器的读数反映了 3D 功率分布的不同方面。本文分析了利用芯内、芯外和热电偶探测器的不同组合重构核心三维功率分布的可行性，以综合各种探测器的有用信息。对多层感知器 (MLP) 网络和径向基函数 (RBF) 网络进行了比较。与 MLP 网络相比，RBF 结果更精确，但对检测器故障和不确定性也更敏感。这是因为局部神经网络可以在 RBF 中提供保守回归。在训练数据集中添加随机干扰有助于减少检测器故障和不确定性的影响。一些卷积神经网络似乎有助于通过使用更多的空间布局信息来获得更准确的结果，尽管相关研究仍在进行中。在训练数据集中添加随机干扰有助于减少检测器故障和不确定性的影响。一些卷积神经网络似乎有助于通过使用更多的空间布局信息来获得更准确的结果，尽管相关研究仍在进行中。在训练数据集中添加随机干扰有助于减少检测器故障和不确定性的影响。一些卷积神经网络似乎有助于通过使用更多的空间布局信息来获得更准确的结果，尽管相关研究仍在进行中。