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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.