In this paper, three individual models and one generalized radial basis function neural network (RBFNN) model were developed for the prediction of the activity concentrations of primordial radionuclides, namely, ²³²Th, ²³⁸U and ⁴⁰K. To achieve this, gamma spectrometry measurements of 126 different geological materials were used in the development of the RBFNN models. The results indicated that individual and generalized RBFNN models are quite efficient in predicting the activity concentrations of ²³²Th, ²³⁸U and ⁴⁰K of geological materials.

中文翻译：

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径向基函数神经网络预测地质材料中232Th、238U和40K的活度浓度

在本文中，开发了三个单独的模型和一个广义径向基函数神经网络 (RBFNN) 模型，用于预测原始放射性核素的活度浓度，即²³² Th、²³⁸ U 和⁴⁰ K。为此，伽马能谱测量在 RBFNN 模型的开发中使用了 126 种不同的地质材料。结果表明，单个和广义 RBFNN 模型在预测地质材料²³² Th、²³⁸ U 和⁴⁰ K 的活度浓度方面非常有效。