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Pearlmutter, Barak A. (1987) Learning state space trajectories in recurrent neural networks. Neural Computation, 1 (2). pp. 263-269. ISSN 0899-7667
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Abstract
Many neural network learning procedures compute gradients of the errors on the output
layer of units after they have settled to their final values. We describe a procedure for finding
aE/aw,, where E is an error functional of the temporal trajectory of the states of a continuous
recurrent network and wy are the weights of that network. Computing these quantities allows
one to perform gradient descent in the weights to minimize E. Simulations in which networks
are taught to move through limit cycles are shown.
| Item Type: | Article |
|---|---|
| Keywords: | state space trajectories; recurrent neural networks; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 5486 |
| Depositing User: | Barak Pearlmutter |
| Date Deposited: | 13 Oct 2014 15:46 |
| Journal or Publication Title: | Neural Computation |
| Publisher: | MIT Press |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/5486 |
| 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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