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
URI:	Publisher
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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