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

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

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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 co-efficients of second-order Volterra filter, a novel Davidon-Fletcher-Powell-based Second-order Volterra filter (DFP-SOVF) 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 DFP-SOVF model to single step predictions for Lorenz chaotic time series illustrate stability and convergence and there have not divergence problems. For the measured multi-frame speech signals, prediction accuracy using DFP-SOVF 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.

ChaosDavidon-Fletcher-Powell algo-rithmPrediction modelSecond-order Volterra filterSpeech signal

ZHANG Yumei、BAI Shulin、LU Gang、WU Xiaojun

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Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China

School of Computer Science, Shaanxi Normal University, Xi'an 710062, China

School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China

This work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaNational Key Research and Development Program of China111 projectFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities

1150213311772178113721672017YFB1402102B18032GK201703082GK201801004

2019

10.1049/cje.2018.04.014

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(1)
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