Error Back Propagation Algorithm with Adaptive Learning Rate

Error Back Propagation algorithm is one of the most popular method for training artificial neural networks. Unfortunately, this is also one of the slowest due to constant and small learning rate parameter used to update weights of neurons. There are therefore many methods for dynamic change this par...

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Veröffentlicht in:The International Conference on Information and Digital Technologies (Online) S. 216 - 222
Hauptverfasser: Kolbusz, Janusz, Rozycki, Pawel, Lysenko, Oleksandr, Wilamowski, Bogdan M.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2019
Schlagworte:
Adaptive learning
Approximation algorithms
Artificial neural networks
Backpropagation
EBP algorithm
Estimation
gradient descent
Heuristic algorithms
learning rate estimation
Neurons
Training
ISSN:2575-677X
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Zusammenfassung:Error Back Propagation algorithm is one of the most popular method for training artificial neural networks. Unfortunately, this is also one of the slowest due to constant and small learning rate parameter used to update weights of neurons. There are therefore many methods for dynamic change this parameter in the adaptive manner. In this paper has been presented new method based on estimation of learning rate with respect of approximation for the value of output error.
ISSN:2575-677X
DOI:10.1109/DT.2019.8813440