MATEC Web Conf.
Volume 193, 2018International Scientific Conference Environmental Science for Construction Industry – ESCI 2018
|Number of page(s)||8|
|Section||Environmentally-Friendly Building Construction|
|Published online||20 August 2018|
Neural network principle of implementation of digital filters
Moscow state University of technology and management. K. G. Razumovsky (Smolensk branch), Lenin street 77, Smolensk region, Vyazma, 215100, Russia
2 Smolensk Humanitarian University, Herzen street 2, 214023, Smolensk, Russia
Corresponding author: firstname.lastname@example.org
Comparative evaluations of the frequency responses (FR) of two types of filters implemented by the classical and neural network methods are carried out. It is shown that the neural network principle of the implementation of digital filters can serve as an alternative to the classical method for specifically defined parameters of FR in the pass bands and attenuation bands of the frequencies of signal spectrum. The simplest method for calculating the parameters of the filters’ difference equations is the neural network approach, regardless of the type of classification of discrete and digital filters. The implementation of TM (transmultiplexer) on a digital element base requires the use of methods of filtering, modulating and demodulating signals that are largely different from traditional analog methods. The frequency responses of non-recursive types of filters presented in the paper are based on the property of the approximable function determined only in the pass bands and attenuation bands of the frequencies of signal spectrum.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.