Back-Propagation Learning of Infinite-Dimensional Dynamical Systems
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概要
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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.
- 社団法人電子情報通信学会の論文
- 1997-02-06
著者
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Tokuda Isao
Department Of Computer Science And Systems Engineering Muroran Institute Of Technology
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Aihara Kazuyuki
Department Of Complexity Science And Engineering Graduate School Frontier Sciences The University Of
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Aihara Kazuyuki
Department Of Complex Science And Engineering Graduate School Of Frontier Science University Of Toky
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Aihara Kazuyuki
Department Of Mathematical Engineering And Information Physics Faculty Of Engineering The University
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