[1]郭海如,李志敏,万兴,等.一种基于随机GA的提高BP网络泛化能力的方法[J].计算机技术与发展,2014,24(01):105-108.
　GUO Hai-ru,LI Zhi-min,WAN Xing,et al.A Method of Improving Generalization for BP Network Based on Random GA[J].,2014,24(01):105-108.
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一种基于随机GA的提高BP网络泛化能力的方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年01期
页码:
105-108
栏目:
智能、算法、系统工程
出版日期:
2014-01-31

文章信息/Info

Title:
A Method of Improving Generalization for BP Network Based on Random GA
文章编号:
1673-629X（2014）01-0105-04
作者:
郭海如; 李志敏; 万兴; 熊斌
湖北工程学院 计算机与信息科学学院
Author(s):
GUO Hai-ru; LI Zhi-min; WAN Xing; XIONG Bin
关键词:
随机遗传算法; 神经网络; 测试误差; 泛化能力
Keywords:
random genetic algorithm; neural network; test error; generalization capability
分类号:
TP181
文献标志码:
A
摘要:
LM-BP网络对其初始权值和阈值敏感，泛化能力不强，针对该缺点，采用遗传算法( GA)对其初始权阈值进行优化，在一定程度上能提高LM-BP网络的泛化能力。为进一步扩展GA初始种群的覆盖范围，进一步提高LM-BP网络的泛化能力，采用多次随机产生初始种群多次优化的方法。以伦河孝感段氟化物含量为实例，建立随机GA的LM-BP网络模型，对原始数据进行拟合及测试，结果表明该方法基本能100%拟合，测试误差不超过2.3%。经过对比实验，证明了该方法的有效性。
Abstract:
The LM-BP neural network was sensitive to its initial weight values and threshold,and it had bad generalization ability. In view of its shortcomings,the initial weights and threshold of LM-BP neural network were optimized with GA. The generalization of LM-BP neural network was improved to a certain extent. To expand the coverage of initial population,the initial populations were randomly generated iteratively and the network was optimized multi times. Thus,the generalization of LM-BP network was further improved. Take the content of fluorine in Lun River from Xiaogan as an example,the LM-BP neural network model based on random GA was estab-lished,and the raw data were fitted and tested. The results showed that the accordance of fitting data is approximately 100%,and the tes-ting errors were less than 2. 3%. Through contrast experiments,the validity of this method was proved.

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更新日期/Last Update: 1900-01-01