Presentation	1997/3/17 Adaptive Annealed RNN Learning of Finite State Automata Ken-ichi Arai, Ryohei Nakano,
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Abstract(in English)	In the learning of finite state automata (FSA) by a recurrent neural network (RNN), we report a new annealed learning method that adaptively adjusts the neuro gain (β) and a learning rate (η). In the previous report, we proved that an BNN can get stable transitions among clusters when β is larger than the critical value and proposed the annealed learning method with a fixed scheduling of β. In the method, however, an error sometimes rapidly grows after a long term learning. Thus, we propose an adaptive annealed learning method to overcome this problem and to get stable cluster transitions faster and stably.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	recurrent neural network / finite state automata / stability / adaptive annealing
Paper #	NC96-130
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Conference Information
Committee	NC
Conference Date	1997/3/17(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)	Adaptive Annealed RNN Learning of Finite State Automata
Sub Title (in English)
Keyword(1)	recurrent neural network
Keyword(2)	finite state automata
Keyword(3)	stability
Keyword(4)	adaptive annealing
1st Author's Name	Ken-ichi Arai
1st Author's Affiliation	NTT Communication Science Laboratories()
2nd Author's Name	Ryohei Nakano
2nd Author's Affiliation	NTT Communication Science Laboratories
Date	1997/3/17
Paper #	NC96-130
Volume (vol)	vol.96
Number (no)	583
Page	pp.pp.-
#Pages	8
Date of Issue