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Authors: André Frank Krause ; Volker Dürr ; Bettina Bläsing and Thomas Schack

Affiliation: University of Bielefeld, Germany

Abstract: Echo State Networks are a special class of recurrent neural networks, that are well suited for attractor-based learning of motor patterns. Using structural multi-objective 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. Both optimization steps can be combined into a nested, hierarchical optimization procedure. 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.

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Paper citation in several formats:
Frank Krause, A.; Dürr, V.; Bläsing, B. and Schack, T. (2010). Evolutionary Optimization of Echo State Networks: Multiple Motor Pattern Learning. In Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2010) - Workshop ANNIIP; ISBN 978-989-8425-03-4, SciTePress, pages 63-71. DOI: 10.5220/0003027500630071

@conference{workshop anniip10,
author={André {Frank Krause}. and Volker Dürr. and Bettina Bläsing. and Thomas Schack.},
title={Evolutionary Optimization of Echo State Networks: Multiple Motor Pattern Learning},
booktitle={Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2010) - Workshop ANNIIP},
year={2010},
pages={63-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003027500630071},
isbn={978-989-8425-03-4},
}

TY - CONF

JO - Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2010) - Workshop ANNIIP
TI - Evolutionary Optimization of Echo State Networks: Multiple Motor Pattern Learning
SN - 978-989-8425-03-4
AU - Frank Krause, A.
AU - Dürr, V.
AU - Bläsing, B.
AU - Schack, T.
PY - 2010
SP - 63
EP - 71
DO - 10.5220/0003027500630071
PB - SciTePress

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