Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks

Author:

Bilski Jarosław1ORCID,Smoląg Jacek1ORCID,Kowalczyk Bartosz1ORCID,Grzanek Konrad2ORCID,Izonin Ivan3ORCID

Affiliation:

1. 1 Department of Computational Intelligence , Częstochowa University of Technology , al. Armii Krajowej 36, 42-200 Częstochowa , Poland

2. 2 Institute of Information Technologies , University of Social Sciences , ul. Sienkiewicza 9, 90-113 Łódź , Poland

3. 3 Department of Artificial Intelligence , Lviv Polytechnic National University Lviv , , Ukraine

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Hardware and Architecture,Modeling and Simulation,Information Systems

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