An optimizing BP neural network algorithm based on genetic algorithm

Ding, Shifei

doi:10.1007/s10462-011-9208-z

2011-02-18

601

PRE

Ding, Shifei *

Su, Chunyang

Junzhao Yu

DOI：10.1007/s10462-011-9208-z

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摘要

En 中文

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.

Keyword:

Genetic algorithm (GA)

BP neural network

Connection weight

UCI data