Presentation 1999/7/19
A Training Method For Mutilayer Nerual Networks Using Global Imformation of Error Surface
Hiroyuki Nose, Kazuyuki Hara,
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Abstract(in English) In this paper, we propose a training method that the convergence of the training does not depend on the initial connection weights. The proposed method uses global information of the error surface. BP algorithm is easily trapped to the local minima, because it uses only local information of the error surface. For this problem, we propose a training method which searches the network expresses the minimum output error by iterating a search process within a region on the error surface. Searching region becomes wider for a large output error and will be narrower for a small output error. This process allows the detail search of the error surface. Moreover, the proposed method updates only one connection weight at one searching process, so this works to reducing the computational complexity.
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Keyword(in English) Global information / Error surface / Search / Annearing / Multilayer neural networks
Paper # NC99-37
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Committee NC
Conference Date 1999/7/19(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) A Training Method For Mutilayer Nerual Networks Using Global Imformation of Error Surface
Sub Title (in English)
Keyword(1) Global information
Keyword(2) Error surface
Keyword(3) Search
Keyword(4) Annearing
Keyword(5) Multilayer neural networks
1st Author's Name Hiroyuki Nose
1st Author's Affiliation Dept. Electrical Eng., Tokyo Metropolitan College of Technology()
2nd Author's Name Kazuyuki Hara
2nd Author's Affiliation Dept. Elec. &Info. Eng., Tokyo Metropolitan College of Technology
Date 1999/7/19
Paper # NC99-37
Volume (vol) vol.99
Number (no) 193
Page pp.pp.-
#Pages 6
Date of Issue