A Novel Method for Training an Echo State Network with Feedback-Error Learning
2013 (English)In: Advances in Artificial Intelligence, ISSN 1687-7470, E-ISSN 1687-7489, Vol. 2013Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2013. Vol. 2013
IdentifiersURN: urn:nbn:no:ntnu:diva-23190DOI: 10.1155/2013/891501OAI: oai:DiVA.org:ntnu-23190DiVA: diva2:658937
Copyright © 2013 Rikke Amilde Lovlid. This is an open access article distributed under theCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.2013-10-232013-10-232013-11-25Bibliographically approved