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Stable Output Feedback in Reservoir Computing Using Ridge Regression

Francis wyffels UGent, Benjamin Schrauwen UGent and Dirk Stroobandt UGent (2008) LECTURE NOTES IN COMPUTER SCIENCE. 5163. p.808-817
abstract
An important property of Reservoir Computing, and signal processing techniques in general, is generalization and noise robustness. In tra jectory generation tasks, we don't want that a small deviation leads to an instability. For forecasting and system identification we want to avoid over-fitting. In prior work on Reservoir Computing, the addition of noise to the dynamic reservoir tra jectory is generally used. In this work, we show that high-performing reservoirs can be trained using only the commonly used ridge regression. We experimentally validate these claims on two very different tasks: long-term, robust tra jectory generation and system identification of a heating tank with variable dead-time.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
regularization, reservoir computing
in
LECTURE NOTES IN COMPUTER SCIENCE
editor
V. Kurkova, R. Neruda and J. Koutnik
volume
5163
issue title
ARTIFICIAL NEURAL NETWORKS - ICANN 2008, PT I
pages
10 pages
publisher
Springer
place of publication
Berlin
conference name
18th International Conference on Arificial Neural Networks (ICANN 2008)
conference location
Prague
conference start
2008-09-03
conference end
2008-09-06
Web of Science type
Proceedings Paper
Web of Science id
000259566200083
ISSN
0302-9743
ISBN
978-3-540-87535-2
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
678847
handle
http://hdl.handle.net/1854/LU-678847
date created
2009-06-05 08:33:54
date last changed
2009-06-30 11:50:40
@inproceedings{678847,
  abstract     = {An important property of Reservoir Computing, and signal processing techniques in general, is generalization and noise robustness. In tra jectory generation tasks, we don't want that a small deviation leads to an instability. For forecasting and system identi\unmatched{fb01}cation we want to avoid over-\unmatched{fb01}tting. In prior work on Reservoir Computing, the addition of noise to the dynamic reservoir tra jectory is generally used. In this work, we show that high-performing reservoirs can be trained using only the commonly used ridge regression. We experimentally validate these claims on two very di\unmatched{fb00}erent tasks: long-term, robust tra jectory generation and system identi\unmatched{fb01}cation of a heating tank with variable dead-time.},
  author       = {wyffels, Francis and Schrauwen, Benjamin and Stroobandt, Dirk},
  booktitle    = {LECTURE NOTES IN COMPUTER SCIENCE},
  editor       = {Kurkova, V. and Neruda, R. and Koutnik, J.},
  isbn         = {978-3-540-87535-2},
  issn         = {0302-9743},
  keyword      = {regularization,reservoir computing},
  language     = {eng},
  location     = {Prague},
  pages        = {808--817},
  publisher    = {Springer},
  title        = {Stable Output Feedback in Reservoir Computing Using Ridge Regression},
  volume       = {5163},
  year         = {2008},
}

Chicago
wyffels, Francis, Benjamin Schrauwen, and Dirk Stroobandt. 2008. “Stable Output Feedback in Reservoir Computing Using Ridge Regression.” In Lecture Notes in Computer Science, ed. V. Kurkova, R. Neruda, and J. Koutnik, 5163:808–817. Berlin: Springer.
APA
wyffels, F., Schrauwen, B., & Stroobandt, D. (2008). Stable Output Feedback in Reservoir Computing Using Ridge Regression. In V. Kurkova, R. Neruda, & J. Koutnik (Eds.), LECTURE NOTES IN COMPUTER SCIENCE (Vol. 5163, pp. 808–817). Presented at the 18th International Conference on Arificial Neural Networks (ICANN 2008), Berlin: Springer.
Vancouver
1.
wyffels F, Schrauwen B, Stroobandt D. Stable Output Feedback in Reservoir Computing Using Ridge Regression. In: Kurkova V, Neruda R, Koutnik J, editors. LECTURE NOTES IN COMPUTER SCIENCE. Berlin: Springer; 2008. p. 808–17.
MLA
wyffels, Francis, Benjamin Schrauwen, and Dirk Stroobandt. “Stable Output Feedback in Reservoir Computing Using Ridge Regression.” Lecture Notes in Computer Science. Ed. V. Kurkova, R. Neruda, & J. Koutnik. Vol. 5163. Berlin: Springer, 2008. 808–817. Print.