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