Presentation | 1994/6/17 Perceptron Back Propagation Learning Based on State Optimization Takeo Ikai, Yasuhiro Yamaguchi, Yoshiaki Kawamura, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This paper presents a formulation of the back propagation(BP) perceptronlearning by means of Hamiltonian functions.It is proved that the weightobtained by the BP method is the optimal weight minimizing Hamiltonianfunctions and BP learning algorithms are iteration methods to find the optimal weight using the gradient method.Next,the state optimization BP learning is derived which updates weights so as to optimize states(outputs of neurons)in each layer of perceptron.This learning method is perfromedby means of an algorithm for optimizing learning rates. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Perceptron / Back-propagation learning / Hamiltonian functions / Hamiltoian minimization / State optimization / Learning rate optimization |
Paper # | NLP94-31 |
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Conference Information | |
Committee | NLP |
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Conference Date | 1994/6/17(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Perceptron Back Propagation Learning Based on State Optimization |
Sub Title (in English) | |
Keyword(1) | Perceptron |
Keyword(2) | Back-propagation learning |
Keyword(3) | Hamiltonian functions |
Keyword(4) | Hamiltoian minimization |
Keyword(5) | State optimization |
Keyword(6) | Learning rate optimization |
1st Author's Name | Takeo Ikai |
1st Author's Affiliation | Faculty of Engineering,University of Osaka Prefecture() |
2nd Author's Name | Yasuhiro Yamaguchi |
2nd Author's Affiliation | Faculty of Engineering,University of Osaka Prefecture |
3rd Author's Name | Yoshiaki Kawamura |
3rd Author's Affiliation | Faculty of Engineering,University of Osaka Prefecture |
Date | 1994/6/17 |
Paper # | NLP94-31 |
Volume (vol) | vol.94 |
Number (no) | 98 |
Page | pp.pp.- |
#Pages | 8 |
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