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Pearlmutter, Barak A. (1990) Dynamic recurrent neural networks. Technical Report. Carnegie Mellon University. (Unpublished)
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Abstract
We survey learning algorithms for recurrent neural networks with hidden units and attempt to put the various techniques into a common framework. We discuss fixpoint learning algorithms, namely recurrent backpropagation and deterministic Boltzmann Machines, and non-fixpoint algorithms, namely backpropagation through time, Elman's history cutoff nets, and Jordan's output feedback architecture. Forward propagation, an online technique that uses adjoint equations, is also discussed. In many cases, the unified presentation leads to generalizations of various sorts. Some simulations are presented, and at the end, issues of computational complexity are addressed.
| Item Type: | Monograph (Technical Report) |
|---|---|
| Keywords: | Dynamic; recurrent; neural; networks; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 5505 |
| Depositing User: | Barak Pearlmutter |
| Date Deposited: | 15 Oct 2014 13:32 |
| Publisher: | Carnegie Mellon University |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/5505 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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