Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%–97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

功率峰值因数（PPF）是反应堆安全高效运行的重要参数。有几种方法可以计算 TRIGA 研究堆的 PPF，例如 MCNP 和 TRIGLAV 代码。然而，这些方法耗时且需要高规格的计算机系统。为了克服这些限制，引入了人工智能来进行参数预测。以前的研究应用神经网络方法来预测 PPF，但使用 ANFIS 方法的出版物尚未得到很好的发展。在本文中，使用 ANFIS 对 PPF 进行了预测。收集两个输入变量，控制棒位置和中子通量，同时使用 TRIGLAV 代码作为数据输出计算 PPF。这些输入输出数据集用于 ANFIS 模型生成、训练和测试。在这项研究中，²值在 96%–97% 的范围内，揭示了预测和实际 PPF 值之间的强关系。计算的 RMSE 也接近于零。从该统计分析中可以看出，ANFIS 可以准确地预测 PPF，并且可以作为一种替代方法来开发 TRIGA 研究堆的实时监测系统。