A serious threat to the integrity of the reactor core, reactor coolant system, or containment is incurred if proper and essential actions to mitigate accidents cannot be taken owing to insufficient information about the internal states of the nuclear power plant (NPP). Therefore, this study was carried out to develop a model capable of mitigating the risk of severe accidents by accurately predicting the internal states of an NPP containment. A deep fuzzy neural network (DFNN) is a method in which syllogistic fuzzy reasoning is relatively efficient and inference capability is enhanced. In this study, the internal states of an NPP containment, hydrogen concentration and pressure, are predicted using a rule-dropout DFNN, as little NPP information is available under the circumstances of severe accidents. In addition, the performance of the proposed rule-dropout DFNN model is compared with that of other fuzzy neural network variations to verify the enhancement in the accuracy of the DFNN. The developed rule-dropout DFNN model is expected to be capable of providing accident monitoring information in advance for accident mitigation, as its prediction error for the hydrogen concentration and pressure in the containment is low.

如果由于关于核电厂（NPP）内部状态的信息不足而无法采取适当和必要的措施来减轻事故，则会对反应堆堆芯，反应堆冷却剂系统或密闭性的完整性造成严重威胁。因此，进行了这项研究以开发能够通过准确预测NPP密闭容器的内部状态来减轻严重事故风险的模型。深度模糊神经网络（DFNN）是一种三段式模糊推理相对有效并且推理能力得到增强的方法。在这项研究中，由于严重事故情况下几乎没有NPP信息，使用规则缺失DFNN可以预测NPP的内部状态，氢浓度和压力。此外，将所提出的规则丢弃DFNN模型的性能与其他模糊神经网络变体的性能进行比较，以验证DFNN准确性的提高。预期开发的规则删除DFNN模型能够为事故缓解提供事故监测信息，因为它对安全壳中氢浓度和压力的预测误差很低。