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On-line Access: 2012-08-02

Received: 2012-01-11

Revision Accepted: 2012-06-21

Crosschecked: 2012-07-06

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.8 P.585-592

10.1631/jzus.C1200008


Negative effects of sufficiently small initial weights on back-propagation neural networks


Author(s):  Yan Liu, Jie Yang, Long Li, Wei Wu

Affiliation(s):  School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China; more

Corresponding email(s):   liuyan@dlpu.edu.cn, yangjiee@dlut.edu.cn, long_li1982@163.com, wuweiw@dlut.edu.cn

Key Words:  Neural networks, Back-propagation, Gradient learning method, Convergence


Yan Liu, Jie Yang, Long Li, Wei Wu. Negative effects of sufficiently small initial weights on back-propagation neural networks[J]. Journal of Zhejiang University Science C, 2012, 13(8): 585-592.

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author="Yan Liu, Jie Yang, Long Li, Wei Wu",
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%A Long Li
%A Wei Wu
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%P 585-592
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1200008

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T1 - Negative effects of sufficiently small initial weights on back-propagation neural networks
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A1 - Long Li
A1 - Wei Wu
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200008


Abstract: 
In the training of feedforward neural networks, it is usually suggested that the initial weights should be small in magnitude in order to prevent premature saturation. The aim of this paper is to point out the other side of the story: In some cases, the gradient of the error functions is zero not only for infinitely large weights but also for zero weights. Slow convergence in the beginning of the training procedure is often the result of sufficiently small initial weights. Therefore, we suggest that, in these cases, the initial values of the weights should be neither too large, nor too small. For instance, a typical range of choices of the initial weights might be something like (−0.4, −0.1)∪(0.1,0.4), rather than (−0.1, 0.1) as suggested by the usual strategy. Our theory that medium size weights should be used has also been extended to a few commonly used transfer functions and error functions. Numerical experiments are carried out to support our theoretical findings.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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