Steady-state performance for the sign normalized algorithm based on Hammerstein spline adaptive filtering

摘要

In this paper, by combining the Hammerstein spline adaptive filtering model with the Li norm minimization criterion, we present a new sign adaptive normalized least mean square algorithm based on Hammerstein spline adaptive filter (HSAF-SNLMS). Meanwhile, the steady-state performance of the proposed algorithm is extensively investigated by application of the energy conservation relation and Price theorem in the case of non-Gaussian noise. Simulation results under the background of the identification of the Hammerstein spline nonlinear system are in good agreement with the theoretical calculations.