Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks
| Autor: | Alina Marchlewska, Jarosław Bilski, Jacek M. Zurada, Bartosz Kowalczyk |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry 02 engineering and technology Levenberg–Marquardt algorithm 020901 industrial engineering & automation Artificial Intelligence Hardware and Architecture Modeling and Simulation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Information Systems |
| Zdroj: | Journal of Artificial Intelligence and Soft Computing Research. 10:299-316 |
| ISSN: | 2083-2567 |
| DOI: | 10.2478/jaiscr-2020-0020 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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