Nonlinear Spline Adaptive Filtering Against Non-Gaussian Noise

In this paper, a generalized maximum Versoria criterion algorithm (GMVC) based on wiener spline adaptive filter, called SAF–GMVC, is proposed. The proposed algorithm is used for nonlinear system identification under non-Gaussian environment. To improve the convergence performance of the SAF–GMVC, the momentum stochastic gradient descent (MSGD) is introduced. In order to further reduce the steady-state error, the variable step-size algorithm is introduced, called as SAF–GMVC–VMSGD. Simulation results demonstrate that SAF–GMVC–VMSGD achieves better filtering effective against non-Gaussian noise.