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FKCN优化的RBF神经网络
引用本文:周佩玲,王上飞,傅忠谦,彭虎,吴耿峰.FKCN优化的RBF神经网络[J].数据采集与处理,1999,14(4):420-423.
作者姓名:周佩玲  王上飞  傅忠谦  彭虎  吴耿峰
作者单位:1. 中国科学技术大学电子科学与技术系,合肥,230026
2. 上海大学计算机工程与科学学院,上海,200072
基金项目:安徽省教委重点资助项目
摘    要:FKCN(Fuzzy Kohonen cluster netw ork)将模糊隶属度的概念用于Kohonen 神经网络的学习和更新策略中,改善了Kohonen 网络的性能,是一种更为快速有效的聚类网络。作者将FKCN用于优化RBF(Radialbasic function)神经网络基函数的中心,并将优化后的RBF网络用于曲线拟合和非线性时间序列预测,同时与基于C-MEANS的RBF网络进行比较。实验结果表明:采用FKCN优化的RBF网络具有更好的拟合和预测能力,尤其在曲线拟合实验中,FKCN优化的RBF网络可以达到最小学习误差,比C-MEANS的网络小一个数量级,可见用FKCN优化RBF神经网络可以较好地提高RBF神经网络的性能。

关 键 词:神经网络  优化  函数逼近  非线性时间序列预测
修稿时间:1999年1月1日

FKCN Optimization of Radial Basis Function Neural Network
Zhou Peiling,Wang Shangfei,Fu Zhongqian,Peng Hu.FKCN Optimization of Radial Basis Function Neural Network[J].Journal of Data Acquisition & Processing,1999,14(4):420-423.
Authors:Zhou Peiling  Wang Shangfei  Fu Zhongqian  Peng Hu
Affiliation:Zhou Peiling Wang Shangfei Fu Zhongqian Peng Hu Department of Electronics Science and Technology,University of Science and Technology of China Hefei,230026 Wu Gengfeng
Abstract:Fuzzy Kohonen cluster network(FKCN) is a fast and effective clustering neural network. It improves Kohonen neural network by using fuzzy membership as its learning rate. This paper provides FKCN to optimize the centers of the radial basis function(RBF) neural network, then applies the network to function approximation and nonlinear time series prediction. Compared with the RBF networks obtained by C MEANS clustering, the optimized RBF network has better approximation and prediction feasibility. Especially in function approximation, the least learning error of the optimized RBF network is about 1/10 of that of the RBF networks obtained by C MEANS clustering.
Keywords:neural networks  optimization  function approximation  nonlinear time series prediction
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