ACL–adaptive correction of learning parameters for backpropagation based algorithms

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschung

Authors

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.
OriginalspracheEnglisch
TitelIJCNN '99, International Joint Conference on Neural Networks : Washington, DC, July 10 - 16, 1999, Proceedings
Anzahl der Seiten4
ErscheinungsortPiscataway
VerlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum07.1999
Seiten1749-1752
ISBN (Print)0-7803-5529-6 , 0-7803-5530-X
DOIs
PublikationsstatusErschienen - 07.1999
VeranstaltungInternational Joint Conference on Neural Networks - 1999 - Washington, DC, USA / Vereinigte Staaten
Dauer: 10.07.199916.07.1999
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6674

DOI

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