[1]窦东阳,杨建国,李丽娟,等.基于规则的神经网络在模式分类中的应用[J].东南大学学报(自然科学版),2011,41(3):482-486.[doi:10.3969/j.issn.1001-0505.2011.03.010]
 Dou Dongyang,Yang Jianguo,Li Lijuan,et al.Application of rule-based neural network in pattern classification[J].Journal of Southeast University (Natural Science Edition),2011,41(3):482-486.[doi:10.3969/j.issn.1001-0505.2011.03.010]
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基于规则的神经网络在模式分类中的应用()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第3期
页码:
482-486
栏目:
自动化
出版日期:
2011-05-20

文章信息/Info

Title:
Application of rule-based neural network in pattern classification
作者:
窦东阳1杨建国1李丽娟2赵英凯2
(1中国矿业大学化工学院,徐州 221116)
(2南京工业大学自动化与电气工程学院,南京 210009)
Author(s):
Dou Dongyang1Yang Jianguo1Li Lijuan2Zhao Yingkai2
(1School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou 221116, China)
(2School of Automation and Electrical Engineering, Nanjing University of Technology, Nanjing 210009, China)
关键词:
模式分类粗糙集遗传算法特征约简神经网络
Keywords:
pattern classification rough set genetic algorithm feature reduction neural network
分类号:
TP18
DOI:
10.3969/j.issn.1001-0505.2011.03.010
摘要:
针对模式分类任务,提出一种基于粗糙集规则的神经网络构造方法.首先,利用粗糙集理论和遗传算法约简输入特征,在尽量保持分类能力不变的情况下降低条件属性维数,并推导出简练的分类规则集合.然后,以规则集为基础构造BP神经网络结构、确定网络层数、输入输出节点数等,并计算规则的条件属性重要度和依赖度2个参数对连接权值进行初始化.最后,通过一个实例验证了方法的有效性,结果表明该方法能有效解决传统神经网络构造难、解释难、过拟合等问题,提高了分类精度,降低了训练时间.此外,初步探讨了网络训练时对知识提炼的影响.
Abstract:
To solve the problems of pattern classification, a method to construct the rough knowledge-based neural network is presented. First, the rough set theory and the genetic algorithm are used for feature reduction of the conditional attributes in order to reduce the number of the inputs without losing too much classification capacity, and the concise rules are generated. Based on the rules, BP(back propagation) network parameters, including number of layers, number of the input/output, connection weights etc. can be determined and initialized together with the two parameters, i. e. the conditional attribute significance and the dependence degree of rules. The method is proved to be valid by an experiment. The result shows that it not only overcomes the disadvantages of traditional neural network such as difficulty in determining the structure, incomprehensibility and over-fitting, but also improves the classification accuracy and reduces the training time. Additionally, the effect of network training on rule refining is discussed.

参考文献/References:

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相似文献/References:

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备注/Memo

备注/Memo:
作者简介:窦东阳(1983—),男,博士,讲师,ddy41@163.com.
基金项目:江苏省自然科学基金资助项目(BK2009356)、江苏省高校自然科学基金资助项目(09KJB510003).
引文格式: 窦东阳,杨建国,李丽娟,等.基于规则的神经网络在模式分类中的应用[J].东南大学学报:自然科学版,2011,41(3):482-486.[doi:10.3969/j.issn.1001-0505.2011.03.010]
更新日期/Last Update: 2011-05-20