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DC Field | Value | Language |
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dc.contributor.author | Vershkov, N. A. | - |
dc.contributor.author | Вершков, Н. А. | - |
dc.contributor.author | Babenko, M. G. | - |
dc.contributor.author | Бабенко, М. Г. | - |
dc.contributor.author | Kuchukov, V. A. | - |
dc.contributor.author | Кучуков, В. А. | - |
dc.contributor.author | Kucherov, N. N. | - |
dc.contributor.author | Кучеров, Н. Н. | - |
dc.contributor.author | Kuchukova, N. N. | - |
dc.contributor.author | Кучукова, Н. Н. | - |
dc.date.accessioned | 2023-01-26T14:32:40Z | - |
dc.date.available | 2023-01-26T14:32:40Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Vershkov, N., Babenko, M., Tchernykh, A., Kuchukov, V., Kucherov, N., Kuchukova, N., Drozdov, A.Yu. Optimization of Artificial Neural Networks using Wavelet Transforms // Programming and Computer Software. - 2022. - 48 (6), pp. 376-384. - DOI: 10.1134/S036176882206007X | ru |
dc.identifier.uri | http://hdl.handle.net/20.500.12258/22262 | - |
dc.description.abstract | The article presents the artificial neural networks performance optimization using wavelet trans- form. The existing approaches of wavelet transform implementation in neural networks imply either transfor- mation before neural network or using “wavenet” architecture, which requires new neural network training approaches. The proposed approach is based on the representation of the neuron as a nonrecursive adaptive filter and wavelet filter application to obtain the low-frequency part of the image. It reduces the image size and filtering interference, which is usually high-frequency. Our wavelet transform model is based on the clas- sical representation of a forward propagation neural network or convolutional layers. It allows designing neu- ral networks with the wavelet transform based on existing libraries and does not require changes in the neural network training algorithm. It was tested on three MNIST-like datasets. As a result of testing, it was found that the speed gain is approximately 50 ± 5% with a slight loss of recognition quality of no more than 4%. For practitioner programmers, the proposed algorithm was tested on real images to distinguish animals and showed similar results as the MNIST-like tests. | ru |
dc.language.iso | en | ru |
dc.relation.ispartofseries | Programming and Computer Software | - |
dc.subject | Artificial neural networks | ru |
dc.subject | Wavelet transforms | ru |
dc.title | Optimization of Artificial Neural Networks using Wavelet Transforms | ru |
dc.type | Статья | ru |
vkr.inst | Факультет математики и компьютерных наук имени профессора Н.И. Червякова | ru |
vkr.inst | Северо-Кавказский центр математических исследований | ru |
Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
Files in This Item:
File | Description | Size | Format | |
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scopusresults 2423 .pdf Restricted Access | 542.46 kB | Adobe PDF | View/Open | |
WoS 1503 .pdf Restricted Access | 113.02 kB | Adobe PDF | View/Open |
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