A Novel Method for Training an Echo State Network with Feedback-Error Learning
Author
Source
Advances in Artificial Intelligence
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-03-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
Echo state networks are a relatively new type of recurrent neural networks that have shown great potentials for solving non-linear, temporal problems.
The basic idea is to transform the low dimensional temporal input into a higher dimensional state, and then train the output connection weights to make the system output the target information.
Because only the output weights are altered, training is typically quick and computationally efficient compared to training of other recurrent neural networks.
This paper investigates using an echo state network to learn the inverse kinematics model of a robot simulator with feedback-error-learning.
In this scheme teacher forcing is not perfect, and joint constraints on the simulator makes the feedback error inaccurate.
A novel training method which is less influenced by the noise in the training data is proposed and compared to the traditional ESN training method.
American Psychological Association (APA)
Løvlid, Rikke Amilde. 2013. A Novel Method for Training an Echo State Network with Feedback-Error Learning. Advances in Artificial Intelligence،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-505843
Modern Language Association (MLA)
Løvlid, Rikke Amilde. A Novel Method for Training an Echo State Network with Feedback-Error Learning. Advances in Artificial Intelligence No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-505843
American Medical Association (AMA)
Løvlid, Rikke Amilde. A Novel Method for Training an Echo State Network with Feedback-Error Learning. Advances in Artificial Intelligence. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-505843
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-505843