Abstract
A Hopfield (1986) type neural network for the design of 1-D FIR filters is proposed. Given the frequency or amplitude response, the all-analog network computes the filter coefficients in real time. The network is simulated with HSPICE and examples are included to show that this is an efficient way of solving the approximation problem compared to the standard techniques for FIR filter design.
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