Real-time design of FIR filters by feedback neural networks

Bhattacharya, D.; Antoniou, A.

doi:10.1109/97.491661

Cited by 34 publications

(25 citation statements)

References 2 publications

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“…It can be seen that the width of the main lobe [4]is inversely proportional to the length of the filter. As the length of the filter is increased, the width of the main lobe becomes narrower and narrower, and the transition band is cut down substantially.…”

Section: Kaiser Windowmentioning

confidence: 96%

Design of Band-Pass Filter using Artificial Neural Network

Dogra¹,

Sharma²

2014

IJCA

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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 (β).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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Section: Kaiser Windowmentioning

confidence: 96%

Design of Band-Pass Filter using Artificial Neural Network

Dogra¹,

Sharma²

2014

IJCA

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“…For the given window, the maximum amplitude of ripple in the filter response is fixed. Thus the stop band attenuation is fixed in the given window, but there are some drawback also of this method [2]. The design of fir filter is not flexible.…”

Section: Design Fir Filter Using Window Methodsmentioning

confidence: 99%

Design Low Pass FIR Filter Using Generalized Regression Neural Network

Gupta¹,

Narwaria²

2014

IJSIP

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“…Bhattacharya and Antoniou [22,23] introduced the design of FIR filters in real-time using feedback neural networks. The disadvantage of this algorithm is the number of neurons required for the network is proportional to the sampling grid of frequency.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, neural networks [21][22][23] have been used for FIR filter design. Bhattacharya and Antoniou [22,23] introduced the design of FIR filters in real-time using feedback neural networks.…”

Section: Introductionmentioning

confidence: 99%