Multirate systems and filter banks
Multirate systems and filter banks
Design of FIR Digital Filters Using Hopfield Neural Network
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Design of FIR Nyquist filters with low group delay
IEEE Transactions on Signal Processing
An iteration scheme for the design of equiripple Mth-band FIRfilters
IEEE Transactions on Signal Processing
A weighted least squares algorithm for quasi-equiripple FIR and IIRdigital filter design
IEEE Transactions on Signal Processing
A design of linear-phased IIR Nyquist filters
IEEE Journal on Selected Areas in Communications
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In this paper, we devised an analog circuit for the weighted least-squares (WLS) design of FIR Nyquist filters by using a Hopfield neural network (HNN). The approach is based on formulating the error function in the optimization of the FIR Nyquist filter as a Lyapunov energy function to find the Hopfield related parameters. By using these parameters and input to the network, the optimal filter coefficients of the FIR Nyquist filter can be derived when the network achieves its convergence. The proposed technique is regular and simple to implement the problems of filter optimization without having the convergence problem as compared to the previous neural-based method. Additionally, the structure proposed can be implemented by using analog VLSI technology in real-time. Simulation results are offered as a suggestion for illustrating the usability of the new method.