权值与结构双确定法的RBF神经网络分类器

摘要

In order to solve the difficulties in determining the weights and structure of the radial basis function (RBF) neural network.Based on the weights-direct-determination (WDD)method and the relationship among centers,variances, the number of hidden-layer neurons and the performance of the neural network,a pruning-while-growing-type weights-and-structure-determination (PWGT-WASD)algorithm is proposed.On the basis of the PWGT-WASD algorithm,a kind of RBF neural network classifier is constructed,and its classifying and antinoise ability is further discussed in this paper.Com-puter numerical experiment results substantiate that the proposed PWGT-WASD algorithm can determine the centers,the va-riances and the optimal weights and structure of RBF neural network quickly and effectively.The constructed RBF pattern classifier has the superiority in terms of classification and antinoise ability.%为了解决径向基函数(RBF)神经网络权值与结构难以确定的问题，基于权值直接确定法，及隐层神经元中心、方差、数目与神经网络性能的关系，提出一种边增边删型的网络权值与结构双确定法。在此方法基础之上，构建一种 RBF神经网络分类器并探讨其分类性能和抗噪能力。计算机数值实验结果验证所提出的边增边删型的权值与结构双确定法能够快速、有效地确定网络的中心、方差和网络最优的权值与结构，所构造的模式分类器具有优越的分类性能和抗噪能力。