Abstract In this study, the estimation of the uranium price as one of the most important factors affecting the fuel cost of nuclear power plants (NPPs) is investigated. Supervised learning algorithms, especially, multilayer feed-forward neural network (FFNN) are used extensively for parameters estimation. Similar to other supervised methods, FFNN can suffer from overfitting (i.e. imbalance between memorization and generalization). In this study, different regularization techniques of FFNN are discussed and the most appropriate regularization technique (i.e. Bayesian regularization) is selected for estimation of the uranium price. The different methods including different learning algorithms of FFNN, support vector machine (SVM) with different kernel functions, radial basis network (RBN), and decision tree (DT) are utilized for the prediction of the uranium price and are compared with FFNN-BR. Average mean relative error (AMRE) and cumulative distribution function (CDF) of the results indicate that FFNN-BR method is more accurate for the uranium price estimation (i.e. CDF (0.0720) = 0.99 and AMRE = 0.0533).

中文翻译：

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与不同监督学习算法相比，基于贝叶斯正则化的多层前馈神经网络估计铀价格的性能研究

摘要 本研究对作为影响核电站燃料成本的最重要因素之一的铀价格的估算进行了研究。监督学习算法，特别是多层前馈神经网络（FFNN）被广泛用于参数估计。与其他监督方法类似，FFNN 可能会出现过拟合（即记忆和泛化之间的不平衡）。在本研究中，讨论了 FFNN 的不同正则化技术，并选择了最合适的正则化技术（即贝叶斯正则化）来估计铀价格。不同的方法包括FFNN的不同学习算法、不同核函数的支持向量机（SVM）、径向基网络（RBN）、和决策树（DT）用于预测铀价格，并与 FFNN-BR 进行比较。结果的平均平均相对误差（AMRE）和累积分布函数（CDF）表明FFNN-BR方法对铀价格估计更准确（即CDF（0.0720）= 0.99和AMRE= 0.0533）。