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Advances in Artificial Intelligence
Volume 2013 (2013), Article ID 891501, 9 pages
Research Article

A Novel Method for Training an Echo State Network with Feedback-Error Learning

Department of Computer and Information Science, Norwegian University of Science and Technology, Sem Sælands vei 7-9, 7491 Trondheim, Norway

Received 31 May 2012; Revised 10 December 2012; Accepted 19 February 2013

Academic Editor: Ralf Moeller

Copyright © 2013 Rikke Amilde Løvlid. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.