Presentation 1998/3/19
Time-domain behavior stabilization of complex-valued recurrent neural networks using relative-minimization learning
Akira Hirose, Hirofumi Onishi,
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Abstract(in English) Relative-minimization learning using additional random teacher signals is newly proposed for time-domain behavior stabilization of recurrent neural networks. Although the recurrent neural networks can deal with time-sequential data, they tend to have an unstable behavior showing positive Lyapunov exponents. Shortly speaking, the proposed method superimposes a type of basin upon a dynamics-determining hypersurface in an information vector field. This process is equivalent to the relative minimization of the error function in the input-signal partial space. Experiments demonstrate that the relative-minimization learning suppresses positive values of Lyapunov exponents down to zero or negative, which means that the behavior in time-domain is successfully stabilized.
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Keyword(in English) Recurrent neural network / Time-domain stability / Lyapunov spectrum
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Committee NC
Conference Date 1998/3/19(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Time-domain behavior stabilization of complex-valued recurrent neural networks using relative-minimization learning
Sub Title (in English)
Keyword(1) Recurrent neural network
Keyword(2) Time-domain stability
Keyword(3) Lyapunov spectrum
1st Author's Name Akira Hirose
1st Author's Affiliation Research Center for Advanced Science and Technology(RCAST), University of Tokyo()
2nd Author's Name Hirofumi Onishi
2nd Author's Affiliation Presently with the NTT visual Communication Sector
Date 1998/3/19
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Volume (vol) vol.97
Number (no) 623
Page pp.pp.-
#Pages 8
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