Reservoir computing with the minimum description length principle.

Abstract

We use the minimum description length (MDL) principle, which is an information-theoretic criterion for model selection, to determine echo-state network readout subsets. We find that this method of MDL subset selection improves accuracy when forecasting the Lorenz, Rössler, and Thomas attractors. It also improves the performance benefit that occurs when higher-order terms are included in the readout layer. We provide an explanation for these improvements in terms of decreased linear dependence and improved consistency.