Presentation | 1997/2/6 Back-Propagation Learning of Infinite-Dimensional Dynamical Systems Isao Tokuda, Kazuyuki Aihara, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This paper studies applicability of back-propagation learning techniques to recurrent neural networks with time-delays (DRNNs). The back-propagation learning is to teach spatib-temporal dynamics to the DRNNs. First, back-propagation learning algorithms are developed for DRNNs. The algorithms are then tested to teach periodic and chaotic dynamics to a class of DRNNs. Comparing with the back-propagation learning of ordinary recurrent neural networks having no time-delay (ORNNs), advantages and disadvantages of the back-propagation learning of DRNNs are discussed. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | back-propagation learning / recurrent neural network / time-delay / infinite-dimensional dynamical system |
Paper # | NLP96-138,NC96-92 |
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Conference Information | |
Committee | NC |
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Conference Date | 1997/2/6(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Back-Propagation Learning of Infinite-Dimensional Dynamical Systems |
Sub Title (in English) | |
Keyword(1) | back-propagation learning |
Keyword(2) | recurrent neural network |
Keyword(3) | time-delay |
Keyword(4) | infinite-dimensional dynamical system |
1st Author's Name | Isao Tokuda |
1st Author's Affiliation | Department of Computer Science and Systems Engineering, Muroran Institute of Technology() |
2nd Author's Name | Kazuyuki Aihara |
2nd Author's Affiliation | Department of Mathematical Engineering and Information Physics, Faculty of Engineering, The University of Tokyo |
Date | 1997/2/6 |
Paper # | NLP96-138,NC96-92 |
Volume (vol) | vol.96 |
Number (no) | 511 |
Page | pp.pp.- |
#Pages | 6 |
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