The application of radial basic function neural network in the data classification is studied.
对径向基函数神经网络在数据分类中的应用进行了研究。
Radial basic function is a recent meshless interpolation technique. It has a very simply form and no correlation with the space dimensions.
径向基函数插值是一种新型的无网格插值方法，具有形式简单、空间维数无关等优点。
Trial numerical computation indicates that taking radial basic function as exciting function of a hidden layer brings good sample fitting effect.
经数值计算结果表明，选择径向基函数作为隐层的激励函数，可以得到较好的样本拟合效果。
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