Work

Multiobjective optimization of echo state networks for multiple motor pattern learning

Year: 2010

Type: conference-paper

Abstract: 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.

Source: PUB – Publications at Bielefeld University (Bielefeld University)

Authors: André Frank Krause, Volker Dürr, Bettina Bläsing, Thomas Schack

Language: English

Topic: Neural Networks and Reservoir Computing

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3411160

FWCI: 0.285

Cites: 12

Cited by: 7

Related to: 10