Please use this identifier to cite or link to this item: https://dspace.ncfu.ru/handle/20.500.12258/4625
Title: Optimization of neural network computation with use of residual number system for tasks of design of neural network systems of automatic control
Authors: Tikhonov, E. E.
Тихонов, Е. Е.
Sosin, A. I.
Сосин, А. И.
Evdokimov, A. A.
Евдокимов, А. А.
Keywords: Chinese remainder theorem (CRT);Data sharing;Generalized polyadic number system (GPNS);Neurocontrol systems with astatic industrial plants;Positional number system (PNS);Residue number system (RNS);Problem solving
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Tikhonov, E.E., Sosin, A.I., Evdokimov, A.A. Optimization of neural network computation with use of residual number system for tasks of design of neural network systems of automatic control // 2018 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2018. - 2018. - Номер статьи 8602679
Series/Report no.: 2018 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2018
Abstract: The article is devoted to the description of approaches to the solution of the actual problem of optimization of neural network calculations using the non-position number system (residual number system) for design problems of neural network automatic control systems. The problems arising in neural network control systems of industrial objects characterized by astatic properties are identified and analyzed. The approach proposed to solve these problems is confirmed by an example of solving a real problem
URI: https://www.scopus.com/record/display.uri?eid=2-s2.0-85061745511&origin=resultslist&sort=plf-f&src=s&st1=Optimization+of+Neural+Network+Computation+with+use+of+Residual+Number+System+for+Tasks+of+Design+of+Neural+Network+Systems+of+&st2=&sid=5ae6e1ef2618948862f9a63c7746ec40&sot=b&sdt=b&sl=142&s=TITLE-ABS-KEY%28Optimization+of+Neural+Network+Computation+with+use+of+Residual+Number+System+for+Tasks+of+Design+of+Neural+Network+Systems+of+%29&relpos=0&citeCnt=0&searchTerm=
http://hdl.handle.net/20.500.12258/4625
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