Presentation | 1998/2/5 Approximation Capability of Neural Networks with Time-Delayed Feedbacks Isao Tokuda, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We present an algorithm for constructing continuous-time recurrent neural network with time delayed feedbacks(DRNN)which approximates the dynamics of any differential difference equation of the form x=F(x(t), x(t-r_1), .., x(t-r_d)). Efficiency of the algorithm is demonstrated by numerical experiments using the Mackey-Glass equation and the Rossler equation. Based on the constructive algorithm, class of dynamical systems approximated by DRNNs is discussed. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | approximation capability / supervised learning / recurrent neural network with time-delayed feedbacks / functional differential equation |
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Conference Information | |
Committee | NLP |
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Conference Date | 1998/2/5(1days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | |
Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Approximation Capability of Neural Networks with Time-Delayed Feedbacks |
Sub Title (in English) | |
Keyword(1) | approximation capability |
Keyword(2) | supervised learning |
Keyword(3) | recurrent neural network with time-delayed feedbacks |
Keyword(4) | functional differential equation |
1st Author's Name | Isao Tokuda |
1st Author's Affiliation | Department of Computer Science and Systems Engineering, Muroran Institute of Technology() |
Date | 1998/2/5 |
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Volume (vol) | vol.97 |
Number (no) | 530 |
Page | pp.pp.- |
#Pages | 5 |
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