Presentation | 2001/10/11 On the Approximation of Hyperbolic Dynamical Systems by Recurrent Neural Networks I.(Continuous Time Systems) Ken-ichi Funabashi, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Theoretical capability of continuous time recurrent neural networks is discussed in this paper. By the use of the approximation theorem on feedforward neural networks and the concept of pseudo-neural systems, mathematical base of approximation of dynamical systems by recurrent neural networks is constructed. Especially, approximation of dynamical systems with hyperbolic attractors by recurrent neural networks is discussed and some results of capability of recurrent neural networks are obtained in the consequence. These results give the theoretical base of application to identification of dynamical systems by recurrent neural networks. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | recurrent neural network / dynamical system / approximation / hyperbolic attractor / chaos |
Paper # | PRMU2001-104,NC2001-54 |
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Conference Information | |
Committee | PRMU |
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Conference Date | 2001/10/11(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | On the Approximation of Hyperbolic Dynamical Systems by Recurrent Neural Networks I.(Continuous Time Systems) |
Sub Title (in English) | |
Keyword(1) | recurrent neural network |
Keyword(2) | dynamical system |
Keyword(3) | approximation |
Keyword(4) | hyperbolic attractor |
Keyword(5) | chaos |
1st Author's Name | Ken-ichi Funabashi |
1st Author's Affiliation | Center for Mathematical Sciences, The University of Aizu() |
Date | 2001/10/11 |
Paper # | PRMU2001-104,NC2001-54 |
Volume (vol) | vol.101 |
Number (no) | 362 |
Page | pp.pp.- |
#Pages | 7 |
Date of Issue |