Second-order Volterra Filter based on DFP Technique

Abstract

Abstract There have some problems caused by selection of improper parameters when LMS, NLMS or RLS algorithms are used to evaluate coefficients of a second-order Volterra filter. We propose a Davidon-Fletcher-Powell-based second-order Volterra filter (DFP-SOVF). The proposed filter is based on a posteriori error assumption and has a variable convergence factor. We give the recursive inverse auto-correlation matrix formulation and present computational complexity analysis. Then applying the proposed DFP-SOVF filter to single step predictions for pure Lorenz chaotic series, prediction results show that the DFP-SOVF filter can guarantee its convergence and stability and there have not divergence problems when using LMS and NLMS algorithms.