RBF

代码说明：

  RBF神经网络：rbf原理：所谓径向基函数(Radial Basis Function 简称 RBF），就是某种沿径向对称的标量函数。通常定义为空间中任一点x到某一中心xc之间欧氏距离的单调函数，可记作 k(||x-xc||），其作用往往是局部的，即当x远离xc时函数取值很小。最常用的径向基函数是高斯核函数，形式为 k(||x-xc||)=exp{- ||x-xc||^2/（2*σ）^2） } 其中xc为核函数中心，σ为函数的宽度参数，控制了函数的径向作用范围。在RBF网络中，这两个参数往往是可调的。(RBF neural network: rbf principle: the so-called radial basis function (Radial Basis Function called RBF), is some kind of radially symmetric scalar function. Is usually defined as the space between any monotonic function to a central point x xc Euclidean distance, may be credited to k (|| x-xc ||), and its role is often partial, that is far the xc function when x The value is very small. The most commonly used is the Gaussian radial basis function kernel function, in the form of k (|| x-xc ||) = exp {- || x-xc || ^ 2/(2* σ) ^ 2)} where xc is Kernel center, σ is the width parameter of the function, the control function of the radial scope. In the RBF network, these two parameters are often adjustable.)

文件列表：

RBF8_6.m,603,2014-10-07
RBF8_7.m,733,2014-10-07