Standard
Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. /
Luttmann, Laurin; Mercorelli, Paolo.
2021 25th International Conference on System Theory, Control and Computing (ICSTCC): October 20 – 23, 2021 Iași, ROMANIA, Proceedings. ed. / Lavinia Ferariu; Mihaela-Hanako Matcovschi; Florina Ungureanu. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2021. p. 234-241 (International Conference on System Theory, Control and Computing; No. 25).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Luttmann, L & Mercorelli, P 2021,
Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. in L Ferariu, M-H Matcovschi & F Ungureanu (eds),
2021 25th International Conference on System Theory, Control and Computing (ICSTCC): October 20 – 23, 2021 Iași, ROMANIA, Proceedings. International Conference on System Theory, Control and Computing, no. 25, Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 234-241, 25th International Conference on System Theory, Control and Computing, Iasi, Romania,
20.10.21.
https://doi.org/10.1109/ICSTCC52150.2021.9607274
APA
Luttmann, L., & Mercorelli, P. (2021).
Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. In L. Ferariu, M.-H. Matcovschi, & F. Ungureanu (Eds.),
2021 25th International Conference on System Theory, Control and Computing (ICSTCC): October 20 – 23, 2021 Iași, ROMANIA, Proceedings (pp. 234-241). (International Conference on System Theory, Control and Computing; No. 25). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/ICSTCC52150.2021.9607274
Vancouver
Luttmann L, Mercorelli P.
Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks. In Ferariu L, Matcovschi MH, Ungureanu F, editors, 2021 25th International Conference on System Theory, Control and Computing (ICSTCC): October 20 – 23, 2021 Iași, ROMANIA, Proceedings. Piscataway: Institute of Electrical and Electronics Engineers Inc. 2021. p. 234-241. (International Conference on System Theory, Control and Computing; 25). doi: 10.1109/ICSTCC52150.2021.9607274
Bibtex
@inbook{a76019e80a18420f93f430f5333137f5,
title = "Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks",
abstract = "This work describes and compares the backpropagation algorithm with the extended Kalman filter (EKF), a second-order training method which can be applied to the problem of learning neural network parameters and is known to converge in only a few iterations. The algorithms are compared with respect to their effectiveness and speed of convergence using simulated data for both, a regression and a classification task.",
keywords = "Backpropagation Algorithm, Kalman Filter, Neural Networks, Engineering",
author = "Laurin Luttmann and Paolo Mercorelli",
year = "2021",
month = oct,
day = "20",
doi = "10.1109/ICSTCC52150.2021.9607274",
language = "English",
isbn = "978-1-6654-3055-5",
series = "International Conference on System Theory, Control and Computing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "25",
pages = "234--241",
editor = "Lavinia Ferariu and Mihaela-Hanako Matcovschi and Florina Ungureanu",
booktitle = "2021 25th International Conference on System Theory, Control and Computing (ICSTCC)",
address = "United States",
note = "25th International Conference on System Theory, Control and Computing, ICSTCC 2021 ; Conference date: 20-10-2021 Through 23-10-2021",
url = "https://ieeexplore.ieee.org/xpl/conhome/9607028/proceeding",
}
RIS
TY - CHAP
T1 - Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks
AU - Luttmann, Laurin
AU - Mercorelli, Paolo
N1 - Conference code: 25
PY - 2021/10/20
Y1 - 2021/10/20
N2 - This work describes and compares the backpropagation algorithm with the extended Kalman filter (EKF), a second-order training method which can be applied to the problem of learning neural network parameters and is known to converge in only a few iterations. The algorithms are compared with respect to their effectiveness and speed of convergence using simulated data for both, a regression and a classification task.
AB - This work describes and compares the backpropagation algorithm with the extended Kalman filter (EKF), a second-order training method which can be applied to the problem of learning neural network parameters and is known to converge in only a few iterations. The algorithms are compared with respect to their effectiveness and speed of convergence using simulated data for both, a regression and a classification task.
KW - Backpropagation Algorithm
KW - Kalman Filter
KW - Neural Networks
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85123317452&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/b493928c-948e-347f-8765-b1efa3182495/
U2 - 10.1109/ICSTCC52150.2021.9607274
DO - 10.1109/ICSTCC52150.2021.9607274
M3 - Article in conference proceedings
AN - SCOPUS:85123317452
SN - 978-1-6654-3055-5
T3 - International Conference on System Theory, Control and Computing
SP - 234
EP - 241
BT - 2021 25th International Conference on System Theory, Control and Computing (ICSTCC)
A2 - Ferariu, Lavinia
A2 - Matcovschi, Mihaela-Hanako
A2 - Ungureanu, Florina
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 25th International Conference on System Theory, Control and Computing
Y2 - 20 October 2021 through 23 October 2021
ER -