[1]张新红,雷素娟.用QCEA优化的RBF神经网络及其在股市预测的应用[J].华侨大学学报(自然科学版),2011,32(3):338-342.[doi:10.11830/ISSN.1000-5013.2011.03.0338]
　ZHANG Xin-hong,LEI Su-juan.Optimized RBF Neural Network and Application in Stock Market Based on Quantum Clonal[J].Journal of Huaqiao University(Natural Science),2011,32(3):338-342.[doi:10.11830/ISSN.1000-5013.2011.03.0338]
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用QCEA优化的RBF神经网络及其在股市预测的应用()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第32卷
期数:
2011年第3期
页码:
338-342
栏目:
出版日期:
2011-05-20

文章信息/Info

Title:
Optimized RBF Neural Network and Application in Stock Market Based on Quantum Clonal
文章编号:
1000-5013(2011)03-0338-05
作者:
张新红; 雷素娟
华侨大学数量经济研究院
Author(s):
ZHANG Xin-hong;  LEI Su-juan
Institute of Mathematical Economics, Huaqiao University, Quanzhou 362021, China
关键词:
径向基函数;  神经网络;  量子克隆进化算法;  股市;  预测
Keywords:
radial basis function;  neural network;  quantum clonal evolutionary algorithm;  stock market;  forecasting
分类号:
TP183
DOI:
10.11830/ISSN.1000-5013.2011.03.0338
文献标志码:
A
摘要:
采用量子克隆进化算法(QCEA)对径向基函数(RBF)神经网络的参数进行优化学习,并通过对不同样本容量和量子旋转角的实验,将量子克隆进化算法优化的径向基函数神经网络应用于上证指数的预测分析中.仿真实验表明:经量子克隆进化算法优化的径向基函数神经网络将全局搜索和局部寻优有机地结合起来,收敛速度快、种群多样性好,并可有效抑制早熟现象.
Abstract:
The paper adopts quantum clonal evolutionary algorithm(QCEA) to optimize the data of radial basis function(RBF) neural network.Based on the practice of testing different samples and quantum rotation angle,the RBF neural network optimized by the QCEA can be applied to analyse the Shanghai stock composite index.The simulation results indicate that the RBF neural network optimized by QCEA can realize the full searching and partial searching for the best,which has a high convergence speed and good group diversity and avoids the premature convergence to some degree.

参考文献/References:

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备注/Memo

备注/Memo:
国务院侨办科研基金资助项目(04QSK05); 华侨大学高层次人才科研启动项目(05BS104)
更新日期/Last Update: 2014-03-23