AB network adjust the step and the hidden-layer neurons algorithm based on BP network

For the classical BP algorithm has some deficiencies, such as the accuracy is insufficient, the rate of convergence does not descend, weight value closes to zero. This paper proposes the AB neural network to adjust the step and the hidden-layer neurons algorithm based on BP network. Network A with l...

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Bibliographic Details
Published inIEEE conference anthology pp. 1 - 4
Main Authors Gong, Ningsheng, Yan Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2013
Subjects
AB neural network
Adaptation models
Artificial intelligence
hidden-layer neurons
Monitoring
Out of order
prior knowledge
step
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DOI10.1109/ANTHOLOGY.2013.6784902

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Summary:For the classical BP algorithm has some deficiencies, such as the accuracy is insufficient, the rate of convergence does not descend, weight value closes to zero. This paper proposes the AB neural network to adjust the step and the hidden-layer neurons algorithm based on BP network. Network A with learning ability configures and adjusts the structure of Network B and trains it, by adjusting the step and the hidden-layer neurons of Network B, obviously enlarge the modification of weight to escape from flat region. The introduction of "prior knowledge" made training of Network B intelligently and automatically. The simulation results of Sin Function shows that the proposed method can effectively speed up the multilayer feed-forward neural network training process.
DOI:10.1109/ANTHOLOGY.2013.6784902