Wavelet Neural Network Optimization Based on Hybrid Hierarchy Genetic Algorithm

被引：1
|
作者：
Yang Xiaobo [1 ]
Feng Jining [2 ]
Diao Zhejun [2 ]
Liu Hongyun [2 ]
机构：
[1] ShiJiaZhuang Profess Technol Inst, Shijiazhuang, Peoples R China
[2] Hebei Normal Univ, Dept Elect Engn, Shijiazhuang, Peoples R China
来源：
MATERIALS SCIENCE AND INFORMATION TECHNOLOGY, PTS 1-8 | 2012年 / 433-440卷
关键词：
wavelet neural network; hybrid hierarchy genetic algorithm; optimization;
D O I：
10.4028/www.scientific.net/AMR.433-440.823
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Based on studying wavelet neural network (WNN) training algorithm and geometrical structure, a new WNN optimization algorithm-hybrid hierarchy genetic is introduced. The algorithm is combined by hierarchy genetic algorithm and linear multi-regress. Hybrid hierarchy genetic algorithm (HHGA) can determine the structure and parameters of WNN from data at one time. The method has the merits of faster learning speed, higher precision. It is compared with traditional BP algorithm in this paper. The effectiveness of the algorithm is demonstrated.
引用
收藏
页码：823 / +
页数：2
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