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。
Then, experiments with the radial basic function and the polynomial kernel function are used to reveal the relationship between kernel functions and the filter algorithm.
接着分别针对径向基核函数和多项式核函数进行多次实验，分析这两种核函数对过滤算法的影响。
Then, experiments with the radial basic function and the polynomial kernel function are used to reveal the relationship between kernel functions and the filter algorithm.
接着分别针对径向基核函数和多项式核函数进行多次实验，分析这两种核函数对过滤算法的影响。
应用推荐