基于ART 的RBF 网络结构设计

针对径向基函数(RBF)网络隐层结构难以确定的问题，基于自适应共振理论(ART)网络良好的在线分类特性，提出一种RBF网络结构设计算法。该算法将ART网络的聚类特性用于RBF网络结构设计中，通过对输入向量与已存模式的相似度比较将输入向量进行分类，确定隐含层节点个数和初始参数，使网络具有精简的结构。对典型非线性函数逼近的仿真结果表明，所提出的结构具有快速的学习能力和良好的逼近能力。

RBF Neural network based on ART neural network

For the problem that it is difficult to determine the hidden layer structure of the radial basis function(RBF) neural network, based on the good online classified characteristic of adaptive resonance theory(ART) neural network, a self-organizing RBF neural network structure design algorithm is proposed. The algorithm uses the clustering characteristic of ART neural network to design the RBF neural network structure. Through the similarity comparison of input vector, the number of the hidden layer nodes and initial parameters are determined, so that the network has simplified structure. The experiment results show that the proposed structure has a smaller number of nodes, fast learning speed and better approximation ability.