Abstract
This special issue illustrates both the scientific trends of the early work in recurrent neural networks, and the mathematics of training when at least some recurrent terms of the network derivatives can be non-zero. Herein is a brief description of each of the papers. We have organized this description into two parts. The first part contains the papers that are mainly theoretical, and the second part contains the papers that are mainly applications. The order of papers is alphabetical by first author.
Keywords
Recurrent neural networkComputer scienceArtificial neural networkArtificial intelligenceTheoretical computer science
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Publication Info
- Year
- 1994
- Type
- article
- Volume
- 5
- Issue
- 2
- Pages
- 153-156
- Citations
- 199
- Access
- Closed
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Cite This
C. Lee Giles,
Gary M. Kuhn,
Ronald J. Williams
(1994).
Dynamic recurrent neural networks: Theory and applications.
IEEE Transactions on Neural Networks
, 5
(2)
, 153-156.
https://doi.org/10.1109/tnn.1994.8753425
Identifiers
- DOI
- 10.1109/tnn.1994.8753425