Design of digital differentiators and Hilbert transformers by feedback neural networks
Dhruba Kumar Bhattacharya, A. Antoniou · 2002
A Hopfield-type neural network is proposed for the design of nonrecursive digital differentiators and Hilbert transformers. Given the amplitude response, the all-analog network computes the filter coefficients in real time. The network is simulated and a few examples are included to show that this is an efficient way of solving the approximation problem and has a high potential for implementation in analog VLSI.