Signal reconstruction from sampled data using neural network

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 original signal even when the Nyquist condition does not stand. Although the proposed method needs weight data, the total data size is much smaller than the ordinary sampling method, as the most suitable reconstruction filter is exclusively adapted to the given sampling data.