Presentation	2005/3/22 The properties of the PB space self-organized by RNNPB learning Kenta YAMADA, Hiroki SUYARI,
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Abstract(in English)	We discuss the characteristic properties of the PB space self-organized by RNNPB learning. In the RNNPB learning, a movement pattern of a robot can be expressed by the corresponding PB value on the PB space, and each pattern can be easily generated by giving each PB value. The PB space is self-organized with keeping correspondence to learning movement patterns. We investigate the characteristic properties of the PB space and the possibility of the application using the PB space.
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Keyword(in English)	recurrent neural network / self-organization / robot's movement pattern
Paper #	NC2004-183
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Conference Information
Committee	NC
Conference Date	2005/3/22(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)	The properties of the PB space self-organized by RNNPB learning
Sub Title (in English)
Keyword(1)	recurrent neural network
Keyword(2)	self-organization
Keyword(3)	robot's movement pattern
1st Author's Name	Kenta YAMADA
1st Author's Affiliation	Graduate School of Science and Technology, Chiba University()
2nd Author's Name	Hiroki SUYARI
2nd Author's Affiliation	Department of Information and Image Science, Chiba University
Date	2005/3/22
Paper #	NC2004-183
Volume (vol)	vol.104
Number (no)	759
Page	pp.pp.-
#Pages	6
Date of Issue