Weighted least-squares design of IIR all-pass filters using a Lyapunov error criterion

This paper extends a neural network based architecture for the weighted least-squares design of IIR all-pass filters. The error difference between the desired phase response and the phase of the designed all-pass filter is formulated as a Lyapunov error criterion. The filter coefficients are obtained when neural network achieves convergence by using the corresponding dynamic function. Furthermore, a weighted updating function is proposed to achieve good approximation to the minimax solution. Simulation results indicate that the proposed technique is able to achieve good performance in a parallelism manner.