期刊
ARTIFICIAL INTELLIGENCE REVIEW
卷 36, 期 2, 页码 153-162出版社
SPRINGER
DOI: 10.1007/s10462-011-9208-z
关键词
Genetic algorithm (GA); BP neural network; Connection weight; UCI data
资金
- (Natural Science Foundation) of Jiangsu Province of China [BK2009093]
- National Nature Science Foundation of China [60975039]
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.
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