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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

Summer 1989, Vol. 1, No. 2, Pages 270-280.
(doi: 10.1162/neco.1989.1.2.270)
© 1989 Massachusetts Institute of Technology
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Article PDF (543.79 KB)
Abstract

The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have (1) the advantage that they do not require a precisely defined training interval, operating while the network runs; and (2) the disadvantage that they require nonlocal communication in the network being trained and are computationally expensive. These algorithms allow networks having recurrent connections to learn complex tasks that require the retention of information over time periods having either fixed or indefinite length.