Presentation 2002/8/16
Implementation of Asynchronous Graded Sta.te Machines through Learning
Akihisa TSUDA, Takanobu NAGANO, Shinobu MIWA, Tomoaki TSUMURA, Masahiro GOSHIMA, Shinji TOMITA,
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Abstract(in English) Recurrent Neural Networks can handle time series information, and are classified as powerful machine: GSM. But it is difficult to lead RNNs to have continuous states and continuous transition through learning. We proposed the way to build GSM on RNN through learning based on BPTT. We implemented a modulo counter on RNN, which recognize continuous input signal. We add an output-input edge with transmission delay to RNN. And we apply EBPTT to time continuous RNN by declining to define teacher signals for particular terms.
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Keyword(in English) Recurrent neural network / time series information / GSM / Learning / BPTT
Paper # CPSY2002-50
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Committee CPSY
Conference Date 2002/8/16(1days)
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Paper Information
Registration To Computer Systems (CPSY)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Implementation of Asynchronous Graded Sta.te Machines through Learning
Sub Title (in English)
Keyword(1) Recurrent neural network
Keyword(2) time series information
Keyword(3) GSM
Keyword(4) Learning
Keyword(5) BPTT
1st Author's Name Akihisa TSUDA
1st Author's Affiliation Grad. School of Informatics Kyoto Univ.()
2nd Author's Name Takanobu NAGANO
2nd Author's Affiliation Grad. School of Informatics Kyoto Univ.
3rd Author's Name Shinobu MIWA
3rd Author's Affiliation Grad. School of Informatics Kyoto Univ.
4th Author's Name Tomoaki TSUMURA
4th Author's Affiliation Grad. School of Economics Kyoto Univ.
5th Author's Name Masahiro GOSHIMA
5th Author's Affiliation Grad. School of Informatics Kyoto Univ.
6th Author's Name Shinji TOMITA
6th Author's Affiliation Grad. School of Informatics Kyoto Univ.
Date 2002/8/16
Paper # CPSY2002-50
Volume (vol) vol.102
Number (no) 276
Page pp.pp.-
#Pages 6
Date of Issue