ZHANG Yumei, BAI Shulin, LU Gang, WU Xiaojun. Kernel Estimation of Truncated Volterra Filter Model Based on DFP Technique and Its Application to Chaotic Time Series Prediction[J]. Chinese Journal of Electronics, 2019, 28(1): 127-135. doi: 10.1049/cje.2018.04.014
Citation: ZHANG Yumei, BAI Shulin, LU Gang, WU Xiaojun. Kernel Estimation of Truncated Volterra Filter Model Based on DFP Technique and Its Application to Chaotic Time Series Prediction[J]. Chinese Journal of Electronics, 2019, 28(1): 127-135. doi: 10.1049/cje.2018.04.014

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

doi: 10.1049/cje.2018.04.014
Funds:  This work is supported by the National Natural Science Foundation of China (No.11502133, No.11772178, No.11372167), the National Key Research and Development Program of China (No.2017YFB1402102), the 111 project (No.B18032), and the Fundamental Research Funds for the Central Universities (No.GK201703082, No.GK201801004).
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  • Corresponding author: WU Xiaojun (corresponding author) is a professor at Shaanxi Normal University. His research interests include pattern recognition, intelligent system and system complexity. (Email:xjwu@snnu.edu.cn)
  • Received Date: 2017-09-17
  • Rev Recd Date: 2017-12-07
  • Publish Date: 2019-01-10
  • 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.
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