Robust group delay equalization of discrete-time filters using neural networks

A novel methodology for first allocating the poles and zeros a group delay equalizer is introduced. In this paper, feed-forward neural networks are used, instead empirical formulae. The results obtained with the networks can be applied as the initial solution of an optimization procedure that searches for the optimum group delay equalizer. Different inputs are considered a priori for the neural networks, but after pre-processing the acquired data, some them are discarded. By evaluating cross-correlation between the inputs and the outputs, the networks are designed through an optimization procedure with mean square error back-propagation, by batch method. Armijo search method is applied in this procedure for improving convergence rate. Simulation results proving the efficiency of the proposed method are presented. Quasi-equiripple group delay responses are obtained with the neural network initial estimate, avoiding local minima and improving the convergence rate of the optimization step of the equalizer designs.