Multiobjective optimization of echo state networks for multiple motor pattern learning
André Frank Krause, Volker Dürr, Bettina E. Bläsing, Thomas Schack · PUB – Publications at Bielefeld University (Bielefeld University) · 2010
Echo State Networks are a special class of recurrent neural networks, that are well-suited for attractorbased learning of motor patterns.Using structural multiobjective optimization, the trade-off between network size and accuracy can be identified.This allows to choose a feasible model capacity for a follow-up full-weight optimization.It is shown to produce small and efficient networks, that are capable of storing multiple motor patterns in a single net.Especially the smaller networks can interpolate between learned patterns using bifurcation inputs.