针对传统的故障诊断方法难以在不确定环境下准确诊断核电厂核反应堆冷却剂系统（reactor coolant system, RCS）故障这一问题，按照以下路线建立了一种核电厂RCS故障诊断动态模糊径向基神经网络（dynamic fuzzy radial basis function neural network, DFRBFNN）模型。首先，根据RCS的故障类型和样本数据，确定DFRBFNN模型的初始结构；然后，应用径向基神经网络方法，构建了RCS故障诊断DFRBFNN初始模型，应用随机初始化方法，对DFRBFNN初始模型的去模糊层到输出层的连接权重进行初始化处理；最后，应用误差下降率法，修正DFRBFNN初始模型的结构和参数，构建了RCS故障诊断DFRBFNN模型。应用所建立的模型对冷却剂丧失、失流和蒸汽发生器管道破裂事故进行诊断，并与传统的故障诊断模型进行对比，验证了本文所建立模型的有效性。研究表明，所构建的核电厂RCS故障诊断DFRBFNN模型能够在不确定环境下准确地诊断RCS的故障。

To address the issue that traditional fault diagnosis methods struggle to accurately diagnose faults in the nuclear reactor coolant system (RCS) of nuclear power plants under uncertain conditions, a dynamic fuzzy radial basis function neural network(DFRBFNN) model was established for RCS fault diagnosis following these steps. First, based on the fault types and sample data of the RCS, the initial structure of the DFRBFNN model was determined. Then, using the radial basis function neural network method, the initial DFRBFNN model for RCS fault diagnosis was constructed, and a random initialization method was applied to initialize the connection weights from the defuzzification layer to the output layer of the initial DFRBFNN model. Finally, the error reduction rate method was used to adjust the structure and parameters of the initial DFRBFNN model, resulting in the final DFRBFNN model for RCS fault diagnosis. The established model was applied to diagnose loss of coolant, flow loss, and steam generator tube rupture accidents, and its performance was compared with traditional fault diagnosis models to verify its effectiveness. The research shows that the constructed DFRBFNN model can accurately diagnose RCS faults under uncertain conditions.