%0 Journal Article
%T Prediction of Chaotic Time Series Based on QPSO:RBF NN<br>基于QPSO—RBF NN的混沌时间序列预测*
%A CHEN Wei
%A FENG Bin
%A SUN Jun
%A <br>陈伟
%A 冯斌
%A 孙俊
%J 计算机应用研究
%D 2007
%I 
%X A novel of method of prediction of chaotic time series based on constructing radial basis function neural network using quantum-behaved particle swarm optimization algorithm was proposed.After determination of units of number in RBF layer,all parameters in relevant network such as central position,spreading constant,weights and offsets of RBF NN were coded to particles in learning algorithm.The parameter vector,which has a best adaptation value,was searched globally.The simulation results show the effectiveness of this method.
%K chaotic series
%K prediction
%K quantum-behaved particle swarm optimization(QPSO)
%K radial basis function neural networks(RBF NN)<br>混沌时间序列
%K 预测
%K 量子粒子群优化算法
%K 径向基函数神经网络
%K 混沌时间序列预测
%K Based
%K Chaotic
%K Time
%K Series
%K 有效性
%K 实例仿真
%K 向量
%K 适应值
%K 最优
%K 搜索
%K 空间
%K 粒子个体
%K 学习算法
%K 编码
%K 权值
%K 输出
%K 常数
%K 扩展
%K 基函数中心
%K 隐层节点数
%K 参数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=EF4AD2E7BFCF0B305FE2B6E186100508&yid=A732AF04DDA03BB3&vid=B91E8C6D6FE990DB&iid=94C357A881DFC066&sid=68D88C2FCF9C3098&eid=09ABD5535D9B6D45&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=2&reference_num=8