Abstract: A new Radial Basis Function（RBF） neural network learning algorithm named center-planed RBFN is presented by introducing plane into the network.In this neural network，the entity of the data center is changed from being a point to that of being a plane to compute the distance between data and data center.The proposed method has its superiority that more information between the data is contained in the center-plane.Experimental results show that the center-planed RBF neural network can achieve better results than traditional RBF neural network in the function approximation and classification.

Key words: center-plane, Radial Basis Function（RBF）, function approximation, classification

摘要： 在传统的径向基神经网络框架的基础上，通过引入中心超平面的概念，提出了超平面中心的径向基函数神经网络。在此网络中以点到中心超平面的距离代替传统的径向基神经网络中点到点的距离，其优势在于中心超平面作为数据中心包含了更多原始数据之间的信息。以函数逼近和数据分类的实验为例，证明了超平面中心的径向基神经网络相对于传统的网络有一定的优势。

关键词: 中心超平面, 径向基函数, 函数逼近, 分类