现代电子技术2016，Vol.39Issue(19)：16-19,24,5.DOI:10.16652/j.issn.1004-373x.2016.19.004
近邻传播聚类算法的RBF隐含层节点优化
Optimization of RBF hidden layer nodes with affinity propagation clustering algorithm
摘要
Abstract
The prediction accuracy of the traditional radial basis function(RBF)neural network may result in lower algo⁃rithm efficiency and pathological numerical value due to the inappropriate random selection of the hidden layer center node,to improve the efficiency of RBF neural network,a method of using affinity propagation(AP)clustering algorithm to improve RBF neural network is proposed. The principle and modeling steps of the method are introduced. Since the adopted AP clustering algo⁃rithm belongs to the self⁃adapting clustering learning algorithm,it needn′t predefine the numbers of the hidden layer center nodes,and is applied to prediction without transcendental information. The AP algorithm is used for clustering iteration according the information of training sample,so as to determine the center node and node numerical value of hidden layer in RBF neural network,and solve the center dereferencing problem of RBF network. After that,all input data is taken in RBF neural network based on AP clustering algorithm for prediction. Since the use of AP algorithm needn′t predefine the clustering numbers,the pro⁃posed scheme can improve the learning accuracy and training speed of the RBF neural network. The approximate simulation ex⁃periment was performed for sine function with the proposed optimization scheme. The results show that the approximate error of the proposed scheme is only 0.005 5,and can keep good prediction accuracy under the noise of 0.3.关键词
径向基函数神经网络/近邻传播聚类算法/隐含层/逼近误差Key words
radial basis function neural network/affinity propagation clustering algorithm/hidden layer/approximate error分类
信息技术与安全科学引用本文复制引用
李志超,孔国利..近邻传播聚类算法的RBF隐含层节点优化[J].现代电子技术,2016,39(19):16-19,24,5.基金项目
国家自然科学基金项目 （）
