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基于混合递阶遗传算法的径向基神经网络学习算法及其应用
引用本文:石红端,刘 勇,刘宝坤,李光泉.基于混合递阶遗传算法的径向基神经网络学习算法及其应用[J].控制理论与应用,2002,19(4):627-630.
作者姓名:石红端  刘 勇  刘宝坤  李光泉
作者单位:1. 天津大学自动化学院,天津,300722
2. 天津大学管理学院,天津300072
摘    要:在研究径向基神经网络学习算法的基础上, 提出了一种新型的径向基神经网络学习算法———混合递阶遗传算法. 该算法将递阶遗传算法和最小二乘法的优点结合在一起, 能够同时确定径向基神经网络的结构和参数, 并具有较高的学习效率. 采用基于混合递阶遗传算法的径向基神经网络对混沌时间序列学习和预测, 取得了较好的效果.

关 键 词:径向基神经网络    混合递阶遗传算法    混沌时间序列
文章编号:1000-8152(2002)04-04-0627
收稿时间:1999/11/29 0:00:00
修稿时间:1999年11月29

RBFNN algorithm based on hybrid hierarchy genetic algorithm and its application
SHI Hong-rui,LIU Yong,LIU Bao-kun and LI Guang-quan.RBFNN algorithm based on hybrid hierarchy genetic algorithm and its application[J].Control Theory & Applications,2002,19(4):627-630.
Authors:SHI Hong-rui  LIU Yong  LIU Bao-kun and LI Guang-quan
Affiliation:School of Automation, Tianjin University, Tianjin 300072, China;School of Automation, Tianjin University, Tianjin 300072, China;School of Automation, Tianjin University, Tianjin 300072, China;School of management, Tianjin University, Tianjin 300072,China
Abstract:Based on the study of RBFNN (radial basis function neural network) training algorithm and genetic algorithm, a new RBFNN training algorithm-hybrid hierarchy genetic algorithm is introduced by combining hierarchy genetic algorithm and least-square method. The hybrid algorithm greatly increases the training speed while is still able to determine the structure and parameters of the RBFNN from sample data. The new training algorithm is used to identify and predict M-G chaos time series, and the simulation gives staisfied result.
Keywords:radial basis function neural network  hybrid hierarchy genetic algorithms  Chaos time series
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