- Tytuł:
- Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks
- Autorzy:
-
Bilski, Jarosław
Smoląg, Jacek
Kowalczyk, Bartosz
Grzanek, Konrad
Izonin, Ivan - Powiązania:
- https://bibliotekanauki.pl/articles/2201329.pdf
- Data publikacji:
- 2023
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
feed-forward neural network
neural network learning algorithm
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation - Opis:
- This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 45--61
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł