Abstract
Two recently proposed approaches to recognize temporal patterns have been proposed by Jäger with the so called Echo State Network (ESN) and by Maass with the so called Liquid State Machine (LSM). The ESN approach assumes a sort of “black-box” operability of the networks and claims a broad applicability to several different problems using the same principle. Here we propose a simplified version of ESNs which we call Simple Echo State Network (SESN) which exhibits good results in memory capacity and pattern matching tasks and which allows a better understanding of the capabilities and restrictions of ESNs.
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References
Jäger, H.: The ”echo state” approach to analysing and training recurrent neural networks. GMD Report 148, GMD - German National Research Institute for Computer Science (2001)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14 (2002)
Jäger, H.: Short term memory in echo state networks. GMD Report 152, GMD - German National Research Institute for Computer Science (2002)
Fette, G.: Signalverarbeitung in Neuronalen Netzen vom Typ Echo State Networks diploma thesis (german) (2004)
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© 2005 Springer-Verlag Berlin Heidelberg
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Fette, G., Eggert, J. (2005). Short Term Memory and Pattern Matching with Simple Echo State Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_3
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DOI: https://doi.org/10.1007/11550822_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28752-0
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