Neuroevolutionary reinforcing learning of neural networks
The article presents the results of combining 4 different types of neural network learning: evolutionary, reinforcing, deep and extrapolating. The last two are used as the primary method for reducing the dimension of the input signal of the system and simplifying the process of its training in terms...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Belarusian National Technical University
2022-01-01
|
| Series: | Системный анализ и прикладная информатика |
| Subjects: | |
| Online Access: | https://sapi.bntu.by/jour/article/view/534 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832557385733898240 |
|---|---|
| author | Y. A. Bury D. I. Samal |
| author_facet | Y. A. Bury D. I. Samal |
| author_sort | Y. A. Bury |
| collection | DOAJ |
| description | The article presents the results of combining 4 different types of neural network learning: evolutionary, reinforcing, deep and extrapolating. The last two are used as the primary method for reducing the dimension of the input signal of the system and simplifying the process of its training in terms of computational complexity.In the presented work, the neural network structure of the control device of the modeled system is formed in the course of the evolutionary process, taking into account the currently known structural and developmental features of self-learning systems that take place in living nature. This method of constructing it makes it possible to bypass the specific limitations of models created on the basis of recombination of already known topologies of neural networks. |
| format | Article |
| id | doaj-art-82845d0fc3044e05ab4063e5b2a28c89 |
| institution | Kabale University |
| issn | 2309-4923 2414-0481 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Belarusian National Technical University |
| record_format | Article |
| series | Системный анализ и прикладная информатика |
| spelling | doaj-art-82845d0fc3044e05ab4063e5b2a28c892025-02-03T05:16:54ZengBelarusian National Technical UniversityСистемный анализ и прикладная информатика2309-49232414-04812022-01-0104162410.21122/2309-4923-2021-4-16-24400Neuroevolutionary reinforcing learning of neural networksY. A. Bury0D. I. Samal1Belarusian State University of Informatics and RadioelectronicsBelarusian State University of Informatics and RadioelectronicsThe article presents the results of combining 4 different types of neural network learning: evolutionary, reinforcing, deep and extrapolating. The last two are used as the primary method for reducing the dimension of the input signal of the system and simplifying the process of its training in terms of computational complexity.In the presented work, the neural network structure of the control device of the modeled system is formed in the course of the evolutionary process, taking into account the currently known structural and developmental features of self-learning systems that take place in living nature. This method of constructing it makes it possible to bypass the specific limitations of models created on the basis of recombination of already known topologies of neural networks.https://sapi.bntu.by/jour/article/view/534neural networksconvolution neural networkneuroevolutionevolutionary algorithmsgenetic algorithmimage recognitioncharacter recognitiontext recognitionneural network trainingdeep learningreinforcement learning |
| spellingShingle | Y. A. Bury D. I. Samal Neuroevolutionary reinforcing learning of neural networks Системный анализ и прикладная информатика neural networks convolution neural network neuroevolution evolutionary algorithms genetic algorithm image recognition character recognition text recognition neural network training deep learning reinforcement learning |
| title | Neuroevolutionary reinforcing learning of neural networks |
| title_full | Neuroevolutionary reinforcing learning of neural networks |
| title_fullStr | Neuroevolutionary reinforcing learning of neural networks |
| title_full_unstemmed | Neuroevolutionary reinforcing learning of neural networks |
| title_short | Neuroevolutionary reinforcing learning of neural networks |
| title_sort | neuroevolutionary reinforcing learning of neural networks |
| topic | neural networks convolution neural network neuroevolution evolutionary algorithms genetic algorithm image recognition character recognition text recognition neural network training deep learning reinforcement learning |
| url | https://sapi.bntu.by/jour/article/view/534 |
| work_keys_str_mv | AT yabury neuroevolutionaryreinforcinglearningofneuralnetworks AT disamal neuroevolutionaryreinforcinglearningofneuralnetworks |