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Content Provider | IEEE Xplore Digital Library |
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Author | Antonelo, E. Schrauwen, B. Stroobandt, D. |
Copyright Year | 2008 |
Description | Author affiliation: Electron. & Inf. Syst. Dept., Ghent Univ., Ghent (Antonelo, E.; Schrauwen, B.; Stroobandt, D.) |
Abstract | In this work we tackle the road sign problem with reservoir computing (RC) networks. The T-maze task (a particular form of the road sign problem) consists of a robot in a T-shaped environment that must reach the correct goal (left or right arm of the T-maze) depending on a previously received input sign. It is a control task in which the delay period between the sign received and the required response (e.g., turn right or left) is a crucial factor. Delayed response tasks like this one form a temporal problem that can be handled very well by RC networks. Reservoir computing is a biologically plausible technique which overcomes the problems of previous algorithms such as backpropagation through time - which exhibits slow (or non-) convergence on training. RC is a new concept that includes a fast and efficient training algorithm. We show that this simple approach can solve the T-maze task efficiently. |
Starting Page | 911 |
Ending Page | 916 |
File Size | 496398 |
Page Count | 6 |
File Format | |
ISBN | 9781424416462 |
ISSN | 10504729 |
DOI | 10.1109/ROBOT.2008.4543321 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2008-05-19 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Mobile robots Robot control Roads Reservoirs Computer networks Recurrent neural networks Learning Biology computing Backpropagation algorithms Computational intelligence |
Content Type | Text |
Resource Type | Article |
Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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