Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/12084
Title: The wave model of artificial neural network
Authors: Vershkov, N. A.
Вершков, Н. А.
Kuchukov, V. A.
Кучуков, В. А.
Kuchukova, N. N.
Кучукова, Н. Н.
Babenko, M. G.
Бабенко, М. Г.
Keywords: Artificial neural network;Correlation function;Spectral analysis;Data communication systems
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Vershkov, N.A., Kuchukov, V.A., Kuchukova, N.N., Babenko, M. The Wave Model of Artificial Neural Network // Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020. - 2020. - Номер статьи 9039172. - Pages 542-547
Series/Report no.: Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
Abstract: The article deals with the modeling of artificial neural networks as a system of information transmission. The analysis of the existing theoretical approaches to the optimization of the structure and training of neural networks is carried out. The proposed model is based on the commonality of decoding processes in information transmission systems and clustering processes in neural networks. In the process of building a neuron model, we consider the well-known problem of determining the bandwidth capacity of the communication channel with noise in the geometric terms and its adaptation to the problem of assigning the input signal to a certain cluster. A neuron is considered to be a universal network element capable of performing orthogonal transformations, filtering, and other transformations of the input sequence. The layer of neurons is considered as an information converter with a certain kernel for solving the problems of orthogonal transformation, matched filtering, and nonlinear transformation for combining the spectra of the input influence of the network and its response. Based on the analysis of the proposed model, it is concluded that it is possible to reduce the number of neurons in the hidden layer and reduce the number of features for training the classifier
URI: http://hdl.handle.net/20.500.12258/12084
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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