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General Information
    • ISSN: 2010-3719 (Online)
    • Abbreviated Title: Int. J. Inf. Electron. Eng.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIEE
    • Editor-in-Chief: Prof. Chandratilak De Silva Liyanage
    • Executive Editor: Jennifer Zeng
    • Abstracting/ Indexing : Google Scholar, Electronic Journals Library, Crossref and ProQuest, Ei (INSPEC, IET), EBSCO.
    • E-mail ijiee@ejournal.net

University of Brunei Darussalam, Brunei Darussalam   
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IJIEE 2013 Vol.3(2): 204-207 ISSN: 2010-3719
DOI: 10.7763/IJIEE.2013.V3.299

Design of Low Pass FIR Filter Using Artificial Neural Network

Harpreet Kaur and Balwinder Dhaliwal

Abstract—In signal processing, there are many instances in which an input signal to a system contains extra unnecessary content or additional noise which can degrade the quality of the desired portion. In such cases we may remove or filter out the useless samples. For example, in the case of the telephone system, there is no reason to transmit very high frequencies since most speech falls within the band of 400 to 3,400 Hz. Therefore, in this case, all frequencies above and below that band are filtered out. The frequency band between 400 and 3,400 Hz, which isn’t filtered out, is known as the pass band, and the frequency band that is blocked out is known as the stop band.[1] Finite Impulse Response, filters are one of the primary types of filters used in Digital Signal Processing. For the design of Low pass FIR filters complex calculations are required. Mathematically, by substituting the values of Pass band, transition width, pass band ripple, stop band attenuation, 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)[2].In this paper, Kaiser Window method has been chosen preferably because of the presence of ripple factor (β). Considering Low pass Filter design, the range of values for the parameters required are calculated. A data sheet through programming is performed on the platform of Matlab. For 30 different range of parameters, the values of h(n) i.e. coefficients of FIR filter, named desired result have been calculated .Artificial Neural Network is a highly simplified model of the structure of the biological neural network. It consists of interconnected processing units. In this thesis, ANN model has been designed which is used to design the low pass FIR which in the specified range of parameter which has been used to train the neural network. Basically, ANN can be trained by many methods like Feed forward neural network, Feedback neural network. But in this is paper the feed forward neural network has been chosen to train the network. Here radial basis function in neural networks is used for the training of the neural network.

Index Terms—Artificial neural network, digital filter, signal processing.

Harpreet Kaur is with BBSBEC, Fatehgarh Sahib (e-mail: mavi_preeti@yahoo.co.in, harpreet.mavi@bbsbec.ac.in)


Cite: Harpreet Kaur and Balwinder Dhaliwal, "Design of Low Pass FIR Filter Using Artificial Neural Network," International Journal of Information and Electronics Engineering vol. 3, no. 2, pp. 204-207, 2013.

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