Kernel Estimation of Truncated Volterra Filter Model Based on DFP Technique and Its Application to Chaotic Time Series Prediction

Abstract

In order to overcome some problems caused by improper parameters selection when applying Least mean square (LMS), Normalized LMS (NLMS) or Recursive least square (RLS) algorithms to estimate coefficients of second-order Volterra filter, a novel DavidonFletcher-Powell-based Second-order Volterra filter (DFPSOVF) is proposed. Analysis of computational complexity and stability are presented. Simulation results of system parameter identification show that the DFP algorithm has fast convergence and excellent robustness than LMS and RLS algorithm. Prediction results of applying DFPSOVF model to single step predictions for Lorenz chaotic time series illustrate stability and convergence and there have not divergence problems. For the measured multiframe speech signals, prediction accuracy using DFPSOVF model is better than that of Linear prediction (LP). The DFP-SOVF model can better predict chaotic time series and the real measured speech signal series.