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ACL–adaptive correction of learning parameters for backpropagation based algorithms

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung

Standard

ACL–adaptive correction of learning parameters for backpropagation based algorithms. / Wilk, Jan; Wilk, Eva; Gobel, H.
IJCNN '99, International Joint Conference on Neural Networks: Washington, DC, July 10 - 16, 1999, Proceedings. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 1999. S. 1749-1752 (International Joint Conference on Neural Networks. Proceedings; Band 3).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung

Harvard

Wilk, J, Wilk, E & Gobel, H 1999, ACL–adaptive correction of learning parameters for backpropagation based algorithms. in IJCNN '99, International Joint Conference on Neural Networks: Washington, DC, July 10 - 16, 1999, Proceedings. International Joint Conference on Neural Networks. Proceedings, Bd. 3, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, S. 1749-1752, International Joint Conference on Neural Networks - 1999, Washington, DC, Washington, USA / Vereinigte Staaten, 10.07.99. https://doi.org/10.1109/IJCNN.1999.832641

APA

Wilk, J., Wilk, E., & Gobel, H. (1999). ACL–adaptive correction of learning parameters for backpropagation based algorithms. In IJCNN '99, International Joint Conference on Neural Networks: Washington, DC, July 10 - 16, 1999, Proceedings (S. 1749-1752). (International Joint Conference on Neural Networks. Proceedings; Band 3). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.1999.832641

Vancouver

Wilk J, Wilk E, Gobel H. ACL–adaptive correction of learning parameters for backpropagation based algorithms. in IJCNN '99, International Joint Conference on Neural Networks: Washington, DC, July 10 - 16, 1999, Proceedings. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 1999. S. 1749-1752. (International Joint Conference on Neural Networks. Proceedings). doi: 10.1109/IJCNN.1999.832641

Bibtex

@inbook{1a357b8f5e3b401a9b2054dabf3190ed,
title = "ACL–adaptive correction of learning parameters for backpropagation based algorithms",
abstract = "We present an improvement of backpropagation learning (BP) for Sigma-Pi networks with adaptive correction of the learning parameters (ACL). An improvement of convergency is achieved by using the information value, change of the output error and the validity of Funahashi's theorem to analytically determine values for the learning parameters momentum, learning rate and learning motivation in each learning step. Its application to a neural-network based approximation of continuous input-output mappings with high accuracy yields very good results: the number of training periods of ACL BP learning is smaller than the corresponding number of training periods using other BP based learning rules.",
keywords = "Informatics",
author = "Jan Wilk and Eva Wilk and H. Gobel",
year = "1999",
month = jul,
doi = "10.1109/IJCNN.1999.832641",
language = "English",
isbn = "0-7803-5529-6 ",
series = "International Joint Conference on Neural Networks. Proceedings",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "1749--1752",
booktitle = "IJCNN '99, International Joint Conference on Neural Networks",
address = "United States",
note = "International Joint Conference on Neural Networks - 1999, IJCNN '99 ; Conference date: 10-07-1999 Through 16-07-1999",
url = "https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6674",

}

RIS

TY - CHAP

T1 - ACL–adaptive correction of learning parameters for backpropagation based algorithms

AU - Wilk, Jan

AU - Wilk, Eva

AU - Gobel, H.

PY - 1999/7

Y1 - 1999/7

N2 - We present an improvement of backpropagation learning (BP) for Sigma-Pi networks with adaptive correction of the learning parameters (ACL). An improvement of convergency is achieved by using the information value, change of the output error and the validity of Funahashi's theorem to analytically determine values for the learning parameters momentum, learning rate and learning motivation in each learning step. Its application to a neural-network based approximation of continuous input-output mappings with high accuracy yields very good results: the number of training periods of ACL BP learning is smaller than the corresponding number of training periods using other BP based learning rules.

AB - We present an improvement of backpropagation learning (BP) for Sigma-Pi networks with adaptive correction of the learning parameters (ACL). An improvement of convergency is achieved by using the information value, change of the output error and the validity of Funahashi's theorem to analytically determine values for the learning parameters momentum, learning rate and learning motivation in each learning step. Its application to a neural-network based approximation of continuous input-output mappings with high accuracy yields very good results: the number of training periods of ACL BP learning is smaller than the corresponding number of training periods using other BP based learning rules.

KW - Informatics

U2 - 10.1109/IJCNN.1999.832641

DO - 10.1109/IJCNN.1999.832641

M3 - Article in conference proceedings

SN - 0-7803-5529-6

SN - 0-7803-5530-X

T3 - International Joint Conference on Neural Networks. Proceedings

SP - 1749

EP - 1752

BT - IJCNN '99, International Joint Conference on Neural Networks

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

T2 - International Joint Conference on Neural Networks - 1999

Y2 - 10 July 1999 through 16 July 1999

ER -

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Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung

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