A novel neural network-based approach for designing 2-D FIR filters

A novel 2-D FIR filter design approach based on neural network optimization (NNO) technique is proposed in the present letter. To demonstrate the feasibility of the NNO design approach, a Tank-Hopfield neural network (THNN) model is chosen and the relation between the MSE (mean square error) criterion and the Lyapunov energy function is also established. The implementation of the approach is described together with some design guidelines. Two 2-D FIR filter design examples are given, and the advantages of the NNO approach over conventional methods are illustrated.