Adaptive finite ring neural network
A new neural network structure of the modular reduction are presented. The new structure is oriented to learning by examples: the number of layers is fixed for any module, the slow approach to the resulting value is excluded, the result is the remainder modulo and does not require an adjustment. Dev...
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Institute of Electrical and Electronics Engineers Inc.
2019
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ir-20.500.12258-71172020-07-07T14:46:14Z Adaptive finite ring neural network Evdokimov, A. A. Евдокимов, А. А. Finite ring neural network Modular reduction Training algorithm A new neural network structure of the modular reduction are presented. The new structure is oriented to learning by examples: the number of layers is fixed for any module, the slow approach to the resulting value is excluded, the result is the remainder modulo and does not require an adjustment. Developed three training algorithm of finite ring neural network: training sample inputs and desired outputs, training only on the desired output and training only for the sampling of input signals. The developed algorithms allow to adapt the finite ring neural network to the new module and, unlike the known solutions, do not require changing the network architecture 2019-09-09T13:13:01Z 2019-09-09T13:13:01Z 2018 Статья Evdokimov, A., Afonin, M. Adaptive finite ring neural network // IEEE 12th International Conference on Application of Information and Communication Technologies, AICT 2018 - Proceedings. - 2018. - Article number 8747166 https://www.scopus.com/record/display.uri?eid=2-s2.0-85070215972&origin=resultslist&sort=plf-f&src=s&st1=Adaptive+finite+ring+neural+network&st2=&sid=00517e04cd9db0cd4826521d40659860&sot=b&sdt=b&sl=50&s=TITLE-ABS-KEY%28Adaptive+finite+ring+neural+network%29&relpos=0&citeCnt=0&searchTerm= http://hdl.handle.net/20.500.12258/7117 en IEEE 12th International Conference on Application of Information and Communication Technologies, AICT 2018 - Proceedings application/pdf application/pdf Institute of Electrical and Electronics Engineers Inc. |
| institution |
СКФУ |
| collection |
Репозиторий |
| language |
English |
| topic |
Finite ring neural network Modular reduction Training algorithm |
| spellingShingle |
Finite ring neural network Modular reduction Training algorithm Evdokimov, A. A. Евдокимов, А. А. Adaptive finite ring neural network |
| description |
A new neural network structure of the modular reduction are presented. The new structure is oriented to learning by examples: the number of layers is fixed for any module, the slow approach to the resulting value is excluded, the result is the remainder modulo and does not require an adjustment. Developed three training algorithm of finite ring neural network: training sample inputs and desired outputs, training only on the desired output and training only for the sampling of input signals. The developed algorithms allow to adapt the finite ring neural network to the new module and, unlike the known solutions, do not require changing the network architecture |
| format |
Статья |
| author |
Evdokimov, A. A. Евдокимов, А. А. |
| author_facet |
Evdokimov, A. A. Евдокимов, А. А. |
| author_sort |
Evdokimov, A. A. |
| title |
Adaptive finite ring neural network |
| title_short |
Adaptive finite ring neural network |
| title_full |
Adaptive finite ring neural network |
| title_fullStr |
Adaptive finite ring neural network |
| title_full_unstemmed |
Adaptive finite ring neural network |
| title_sort |
adaptive finite ring neural network |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2019 |
| url |
https://www.scopus.com/record/display.uri?eid=2-s2.0-85070215972&origin=resultslist&sort=plf-f&src=s&st1=Adaptive+finite+ring+neural+network&st2=&sid=00517e04cd9db0cd4826521d40659860&sot=b&sdt=b&sl=50&s=TITLE-ABS-KEY%28Adaptive+finite+ring+neural+network%29&relpos=0&citeCnt=0&searchTerm= https://dspace.ncfu.ru/handle/20.500.12258/7117 |
| work_keys_str_mv |
AT evdokimovaa adaptivefiniteringneuralnetwork AT evdokimovaa adaptivefiniteringneuralnetwork |
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1760599324007333888 |