Local Levenberg-Marquardt algorithm for learning feedforwad neural networks

• Autorzy: Bilski Jarosław , Kowalczyk Bartosz , Marchlewska Alina , Zurada Jacek M.

Identyfikatory

DOI

10.2478/jaiscr-2020-0020

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

feed-forward neural network; neural network learning algorithm; optimization problem; Levenberg-Marquardt algorithm; QR decomposition; Givens rotation

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2020
• Tom: Vol. 10, No. 4
• Strony: 299--316
• Opis fizyczny: Bibliogr. 46 poz., rys.

Twórcy

autor

Bilski Jarosław

• Department of Computer Engineering, Czestochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Kowalczyk Bartosz

• Department of Computer Engineering, Czestochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Marchlewska Alina

• University of Social Science, Łodź, Poland
• Clark University Worcester, MA, USA

autor

Zurada Jacek M.

• Department Electrical and Computer Engineering, University of Louisville, Louisville, KY 40292, USA

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).