Design of Low Pass FIR Filter Using Artificial Neural Network
Keywords:
Artificial neural network, digital filter, signal processing.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
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