Stable Output Feedback in Reservoir Computing Using Ridge Regression
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Self-organizing multiple models for imitation: teaching a robot to dance the YMCA
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
A novel method for training an echo state network with feedback-error learning
Advances in Artificial Intelligence
| Hi-index | 0.00 |
When an echo state network with feedback connections is trained with teacher forcing and later run in free mode, one often gets problems with stability. In this paper an echo state network is trained to execute an arm movement. A sequence with the desired coordinates of the limbs in each time step is provided to the network together with the current limb coordinates. The network must find the appropriate angle velocities that will keep the arms on this trajectory. The current limb coordinates are indirect feedback from the motor output via the simulator. We do get a problem with stability in this setup. One simple remedy is adding noise to the internal states of the network. We verify that this helps, but we also suggest a new training strategy that leeds to even better performance on this task.