Open AccessKernel Estimation of Truncated Volterra Filter Model Based on DFP Technique and Its Application to Chaotic Time Series Prediction
Author(s) -
Zhang Yumei,
Bai Shulin,
Lu Gang,
Wu Xiaojun
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.04.014
Subject(s) - kernel (algebra) , chaotic , volterra series , filter (signal processing) , computer science , series (stratigraphy) , control theory (sociology) , mathematics , algorithm , nonlinear system , artificial intelligence , physics , computer vision , discrete mathematics , paleontology , control (management) , quantum mechanics , biology
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.