径向基函数神经网络在散乱数据插值中的应用

Applications of Radius Basis Function Neural Networks in Scattered Data Interpolation

针对径向基函数 (RBF)神经网络的特点 ,结合网络设计工作 ,对计算机辅助几何设计 (简称CAGD)中的散乱数据插值和曲面上离散点集的光滑插值问题 ,采用RBF神经网络进行求解 .从应用结果来看 ,RBF网络适合于解决曲面离散点集的光滑插值问题 ,比传统的样条方法更有效、更方便 ,具有较好的使用价值 ,并且可以很容易地推广到求解高维散乱数据插值问题之中 .

Radius basis function(RBF) neural network is applied in scattered data interpolation and smooth interpolation of scattered data on a surface. The experimental results, show that the RBF neural network is suitable for solving the problem of scattered data interpolation on a surface, and is more efficient than traditional methods. Furthermore, it can be easily generalized to solve the scattered data interpolation problem in higher dimensions.