A design for variable fractional delay FIR filters

The paper proposes a neural network method to design variable fractional delay finite impulse response filters. This design is achieved by choosing a continuous Hopfield neural network (CHNN) model and establishing the relation between the MSE criterion and the Lyapunov energy function. In an illustrative design example, the proposed method has improved the filter performance with slightly increased computation cost, as compared with a weighted least-squares design method.