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Signal reconstruction from sampled data using neural network
Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing
For reconstructing the signal from sampling data, the method based on Shannon's Sampling theorem is usually employed. In this method, the reconstruction error appears when the signal does not satisfy the Nyquist condition. This paper proposes a new reconstruction method by using a linear perceptron and multi layer perceptron as FIR filter. The perceptron which has the weights obtained by learning in adapting the original signal suppresses the difference between the reconstructed signal and the
doi:10.1109/nnsp.2002.1030082
dblp:conf/nnsp/SudouHSH02
fatcat:5x2mth7i4bchzc5dck5ropp5ce