Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks

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 signi...

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Bibliographic Details
Published in:Journal of Artificial Intelligence and Soft Computing Research 2020-10, Vol.10 (4), p.299-316
Main Authors: Bilski, Jarosław, Kowalczyk, Bartosz, Marchlewska, Alina, Zurada, Jacek M
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Machine learning
Neural networks
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Summary: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.
ISSN:2449-6499
DOI:10.2478/jaiscr-2020-0020