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An optimizing BP neural network algorithm based on genetic algorithm

文献类型：期刊论文

作者Ding, Shifei1,2; Su, Chunyang1; Yu, Junzhao1
刊名ARTIFICIAL INTELLIGENCE REVIEW
出版日期2011-08-01
卷号36期号:2页码:153-162
关键词Genetic algorithm (GA) BP neural network Connection weight UCI data
ISSN号0269-2821
DOI10.1007/s10462-011-9208-z
英文摘要A back-propagation (BP) neural network has good self-learning, self-adapting and generalization ability, but it may easily get stuck in a local minimum, and has a poor rate of convergence. Therefore, a method to optimize a BP algorithm based on a genetic algorithm (GA) is proposed to speed the training of BP, and to overcome BP's disadvantage of being easily stuck in a local minimum. The UCI data set is used here for experimental analysis and the experimental result shows that, compared with the BP algorithm and a method that only uses GA to learn the connection weights, our method that combines GA and BP to train the neural network works better; is less easily stuck in a local minimum; the trained network has a better generalization ability; and it has a good stabilization performance.
资助项目(Natural Science Foundation) of Jiangsu Province of China[BK2009093] ; National Nature Science Foundation of China[60975039]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000292555100005
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/12841]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ding, Shifei
作者单位1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221008, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714		 Ding, Shifei,Su, Chunyang,Yu, Junzhao. An optimizing BP neural network algorithm based on genetic algorithm[J]. ARTIFICIAL INTELLIGENCE REVIEW,2011,36(2):153-162.
APA Ding, Shifei,Su, Chunyang,&Yu, Junzhao.(2011).An optimizing BP neural network algorithm based on genetic algorithm.ARTIFICIAL INTELLIGENCE REVIEW,36(2),153-162.
MLA Ding, Shifei,et al."An optimizing BP neural network algorithm based on genetic algorithm".ARTIFICIAL INTELLIGENCE REVIEW 36.2(2011):153-162.

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来源：计算技术研究所

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