Presentation 1994/3/25
Relations between Sigmoid function′s Polarity and Convergence in B ack Propagetaion Learning
Hiromu Gotanda, Yoshihiro Ueda, Takeshi Kawasaki, Nobuyuki Tanaka,
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Abstract(in English) This paper studies how the difference of sigmoidal function′s po larity effects the convergence properties of the error back propagation learning.From simulation results,the convergence properties are summarized as follows.First,in the case of large sized networks,the bipolar sigmoid is superior in both convergence ratios and learning speeds to the unipolar one,and gives good convergence for wider range of initial values than the latter.Then, the convergence properties due to the unipolar sigmoid are apt to depend on the learning rate more than those due to the bipolar one. Finally,in the case of small sized networks,the initial values giving good convergence are smaller for the unipolar sigmoid than for the bipolar one.
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Keyword(in English) multilayer network / error back propagation / polarity of sigmoid functions / convergenvcy
Paper # NC93-115
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Committee NC
Conference Date 1994/3/25(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Relations between Sigmoid function′s Polarity and Convergence in B ack Propagetaion Learning
Sub Title (in English)
Keyword(1) multilayer network
Keyword(2) error back propagation
Keyword(3) polarity of sigmoid functions
Keyword(4) convergenvcy
1st Author's Name Hiromu Gotanda
1st Author's Affiliation Faculty of Engineering,in Kyushu,Kinki University()
2nd Author's Name Yoshihiro Ueda
2nd Author's Affiliation Faculty of Engineering,in Kyushu,Kinki University
3rd Author's Name Takeshi Kawasaki
3rd Author's Affiliation Faculty of Engineering,in Kyushu,Kinki University
4th Author's Name Nobuyuki Tanaka
4th Author's Affiliation Faculty of Engineering,in Kyushu,Kinki University
Date 1994/3/25
Paper # NC93-115
Volume (vol) vol.93
Number (no) 537
Page pp.pp.-
#Pages 8
Date of Issue