Presentation	1994/10/13 Learning Algorithm for Elman Networks based on Quasi-Newton Method Kazumi Saito, Ryohei Nakano,
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Abstract(in English)	The BPTT and RTRL algorithms are representative learning methods for recurrent neural networks.Since these methods,however,are based on the steepest-descent algorithm,they often require a large number of iterations for convergence.In this paper,we propose a new learning algorithm called BPTTQ,which employs an efficient calculation of the optimal step lengths as the minimal points of an approximation.Experiments showed that BPTTQ worked much better than the existing algorithms.
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Keyword(in English)	Elman networks / BPTT / RTRL / quasi-Newton method / optimal step-length
Paper #	NC94-38
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Conference Information
Committee	NC
Conference Date	1994/10/13(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Learning Algorithm for Elman Networks based on Quasi-Newton Method
Sub Title (in English)
Keyword(1)	Elman networks
Keyword(2)	BPTT
Keyword(3)	RTRL
Keyword(4)	quasi-Newton method
Keyword(5)	optimal step-length
1st Author's Name	Kazumi Saito
1st Author's Affiliation	Science Laboratories,NTT Communication()
2nd Author's Name	Ryohei Nakano
2nd Author's Affiliation	Science Laboratories,NTT Communication
Date	1994/10/13
Paper #	NC94-38
Volume (vol)	vol.94
Number (no)	272
Page	pp.pp.-
#Pages	8
Date of Issue