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Reseach Article

Design of Band-Pass Filter using Artificial Neural Network

by Shushank Dogra, Narinder Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 1
Year of Publication: 2014
Authors: Shushank Dogra, Narinder Sharma
10.5120/15465-3837

Shushank Dogra, Narinder Sharma . Design of Band-Pass Filter using Artificial Neural Network. International Journal of Computer Applications. 89, 1 ( March 2014), 13-18. DOI=10.5120/15465-3837

@article{ 10.5120/15465-3837,
author = { Shushank Dogra, Narinder Sharma },
title = { Design of Band-Pass Filter using Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 1 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number1/15465-3837/ },
doi = { 10.5120/15465-3837 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:06.582012+05:30
%A Shushank Dogra
%A Narinder Sharma
%T Design of Band-Pass Filter using Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 1
%P 13-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For the design of Band pass FIR filters complex calculations are involved. Mathematically, by substituting the value of pass-band ripple, stop band attenuation, pass-band frequency F1, pass-band frequency F2, sampling frequency in any of the methods from window method, frequency sampling method or optimal method we can get the values of filter coefficients h(n). Here, window method is used in which Kaiser window method has been chosen preferably because of the presence of ripple factor (?). Here, I have design Band pass FIR filter using artificial neural network which gives optimum result i. e. the difference between the actual and desired output is minimum.

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Index Terms

Computer Science
Information Sciences

Keywords

Window functions Artificial neural network.