基于参数优化的RBF神经网络结构设计算法

针对径向基函数(RBF)神经网络的结构优化问题，提出了一种基于参数优化的RBF神经网络优化算法.首先，改进K-means++算法，使得聚类算法更精确，为RBF神经网络的隐含层节点找到一个合适的初始中心;然后，考虑数据分布和缩放因子选择的影响，采用方差度量法计算隐含层神经元基函数的宽度;最后，修正网络参数，提高网络的非线性逼近能力.实验结果表明，本文提出的基于参数优化的RBF神经网络具有良好的逼近效果和泛化能力.

Algorithm for Structure Design of RBF Neural Network Based on Parameter Optimization

An algorithm based on parameter optimization(KV-RBF)is proposed for optimizing RBF(radial basis function) neural network structure. Firstly， the K-means++ algorithm， which makes the clustering algorithm more accurate， is improved to find an appropriate initial center for the hidden layer node of RBF neural network. Then， considering the influence of data distribution and scaling factor selection， the width of neuron basis function in the hidden layer is calculated by variance measurement. Finally， the network parameters are modified to improve the nonlinear approximation ability of the network. The experimental results show that the proposed RBF neural network based on parameter optimization has good approximation effect and generalization ability.