By Topic

Two constructive algorithms for improved time series processing with recurrent neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Bone, R. ; Lab. d''Inf., Univ. de Tours, France ; Crucianu, M. ; Asselin de Beauville, J.-P.

Because of their universal approximation capabilities, recurrent neural networks are an attractive choice for building models of time series out of available data. Medium- and long-term dependencies are easier to learn when the recurrent network contains time-delayed connections. We propose two constructive algorithms which are able to choose the right locations and delays of such connections. To evaluate the capabilities of these algorithms, we use both natural data and synthetic data having built-in time delays. We then compare the two algorithms in order to define their domain of interest. The results we obtain on several benchmarks show that, by selectively adding a few time-delayed connections to recurrent networks, one is able to improve upon the results reported in the literature, while using significantly fewer parameters

Published in:

Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop  (Volume:1 )

Date of Conference: