Choosing of neural network architecture for electronic differential system of electric vehicle
- Authors: Lisov A.A.1, Vozmilov A.G.1, Gundarev K.A.1
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Affiliations:
- South Ural State University
- Issue: Vol 10, No 4 (2024)
- Pages: 446-462
- Section: Reviews
- URL: https://bakhtiniada.ru/transj/article/view/277918
- DOI: https://doi.org/10.17816/transsyst635379
- ID: 277918
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Abstract
Aim. To study various network architecture options for implementing an electronic differential system in an electric vehicle.
Materials and Methods. The study primarily used comparative analysis to identify the most rational neural network (NN) architecture for processing numerical data structured as arrays.
Results. The analysis revealed that a deep learning neural network is the most effective choice. For future developments, and after experimental confirmation, a recurrent neural network could also be a viable option.
Conclusion. The study confirmed that achieving the desired goal is not feasible using convolutional neural networks, large language models, random vector functional communication networks and radial-basis functional NNs.
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##article.viewOnOriginalSite##About the authors
Andrey A. Lisov
South Ural State University
Author for correspondence.
Email: lisov.andrey2013@yandex.ru
ORCID iD: 0000-0001-7282-8470
SPIN-code: 1956-3662
postgraduate student
Russian Federation, ChelyabinskAlexander G. Vozmilov
South Ural State University
Email: vozmiag@rambler.ru
ORCID iD: 0000-0002-1292-3975
SPIN-code: 2893-8730
Professor, Doctor of Technical Sciences
Russian Federation, ChelyabinskKirill A. Gundarev
South Ural State University
Email: pioneer03.95@mail.ru
ORCID iD: 0009-0004-8358-1329
SPIN-code: 1238-1158
postgraduate student
Russian Federation, ChelyabinskReferences
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Supplementary files
Supplementary Files
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Fig. 1. Structure of an electronic differential system using a neural network [1]
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Fig. 2. a) A general topology of ANN, b) Feedforward ANN (perceptron), c) Feedback ANN
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Fig. 3. Architectures of neural networks. a) DNN, b) regular NN
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Fig. 4. The architecture of RVFL [29]
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Fig. 5. Architecture of a radial basis function network [35]
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Fig. 6. Architecture of the developed deep learning neural network
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Fig. 7. Prediction graphs of the neural network model on the test sample [1]
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