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Artificial Neural Network Use for Design Low Pass FIR Filter a Comparison

M. A. Singh and V. B. V. Thakare
Madhav Institute of Technology and Science/Dept. of Electronics Engineering, Gwalior, India
Abstract—The present paper investigates an approach for comparison of different types of artificial neural network used in design and analysis of low pass FIR filter. The simulated values for training and testing the neural network are obtained by designing low pass FIR filter with hamming window method using FDA toolbox in MATLAB. As hamming window is an optimized window method which can minimize the maximum (nearest) side lobe of a signal hence hamming window method is preferred in this work. In this paper three different algorithm of artificial neural network namely generalized regression method, feed forward back propagation and radial basis function are used. The result obtained using artificial neural network are compared and radial basis function founds to give quite satisfactory result then generalized regression method and feed forward back propagation method.
 
Index Terms—FIR filter, ANN, GRNN, BPNN, RBF

Cite: M. A. Singh and V. B. V. Thakare, "Artificial Neural Network Use for Design Low Pass FIR Filter a Comparison," International Journal of Electronics and Electrical Engineering, Vol. 3, No. 3, pp. 216-219, June 2015. doi: 10.12720/ijeee.3.3.216-219
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