| Presentation | 1997/7/24 Unique Represetations of Dynamical Systems Produced by Recurrent Nets Masahiro KIMURA, Ryohei NAKANO, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper considers learning a dynamical system by a recurrent neural network (RNN). We propose an affine neural dynamical system (A-NDS) as a dynamical system that an RNN actually produces on the output space to approximate a target dynamical system. We present a unique parametric representation of A-NDSs using RNNs and their affine sections with the aim of constructing effective learning algorithms. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | learning of dynamical systems / recurrent net / affine neural dynamical system / uniqure representation |
| Paper # | NC97-26 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 1997/7/24(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Unique Represetations of Dynamical Systems Produced by Recurrent Nets |
| Sub Title (in English) | |
| Keyword(1) | learning of dynamical systems |
| Keyword(2) | recurrent net |
| Keyword(3) | affine neural dynamical system |
| Keyword(4) | uniqure representation |
| 1st Author's Name | Masahiro KIMURA |
| 1st Author's Affiliation | NTT Communication Science Laboratories() |
| 2nd Author's Name | Ryohei NAKANO |
| 2nd Author's Affiliation | NTT Communication Science Laboratories |
| Date | 1997/7/24 |
| Paper # | NC97-26 |
| Volume (vol) | vol.97 |
| Number (no) | 201 |
| Page | pp.pp.- |
| #Pages | 8 |
| Date of Issue | |