一种新型径向基函数神经网络学习算法——递归正交最小二乘法(ROLS)

A New Learning Algorithm Based on Radial Basis Function Neural Network --Recursive Orthogonal Least Squares (ROLS) Algorithm

径向基函数神经网络在很多领域都得到了成功的应用。但迄今为止仍没有一种有效的方法来确定隐层中心数目。笔者将递归正交最小二乘 (ROLS)方法引入RBFNN建模训练 ,利用ROLS算法训练网络后所得的有用信息 ,采用后向选择算法 ,逐步去掉那些使网络残差增加最小的中心 ,在得到网络有效中心的同时 ,还满足了精度要求 ,从而大大简化了RBF网络结构 ,节约了大量的存储空间以及计算量。

The Recursive Orthogonal Least Squares (ROLS) algorithm is applied to train Radial Basis Function Neural Network (RBFNN) when modeling, so as to save large memory and computational efforts. Using the information available from the trained network with ROLS algorithm, the effective centers of network can be obtained by adopting backward selection algorithm, which achieve acceptable accuracy with significant reduction of network structure. The results of simulation and experimentation show that the algorithm is efficient and useful.