Hammerstein-Spline Adaptive Filtering Architecture Based on Gradient Weighted Average Algorithm

This paper presents a Hammerstein-Spline adaptive filtering architecture using gradient weighted average approach. The proposed Hammerstein-spline adaptive filtering (HSAF) architecture is explained shortly. Basic structure consists of 2 coefficients of the nonlinear coefficient connected with the linear coefficient. Gradient weighted average (GWA) approach and adaptive step-size mechanism are employed on HSAF to guarantee low computation with smooth convergence. In short, results demonstrate that the proposed HSAF based on GWA algorithm with averaging adaptive step-size approach can converge and reduce the estimated error rate compared with the existing algorithm.