- Tytuł:
- Local Levenberg-Marquardt algorithm for learning feedforwad neural networks
- Autorzy:
-
Bilski, Jarosław
Kowalczyk, Bartosz
Marchlewska, Alina
Zurada, Jacek M. - Powiązania:
- https://bibliotekanauki.pl/articles/1837415.pdf
- Data publikacji:
- 2020
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
feed-forward neural network
neural network learning algorithm
optimization problem
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation - Opis:
- This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 299-316
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł