A DFP-Based Second-Order Volterra Model with Applications to Rössler Chaotic Series Prediction

ZHANG Yu-mei; WU Xiao-jun; BAI Shu-lin

doi:10.3969/j.issn.0372-2112.2014.09.021

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A DFP-Based Second-Order Volterra Model with Applications to Rössler Chaotic Series Prediction

更新时间：2025-07-16

- A DFP-Based Second-Order Volterra Model with Applications to Rössler Chaotic Series Prediction
- Acta Electronica Sinica Vol. 42, Issue 9, Pages: 1801-1806(2014)
- 作者机构：
  
  1. 陕西师范大学计算机科学学院,陕西,西安,710062
  2. 西北工业大学自动化学院,陕西,西安,710072
  3. 西北工业大学电子信息学院,陕西,西安,710072
  4. 陕西师范大学计算机科学学院,陕西,西安,710062
  5. 西北工业大学自动化学院,陕西,西安,710072
  6. 西北工业大学电子信息学院,陕西,西安,710072
- 作者简介：
- 基金信息：
  
  Program for New Century Excellent Talents in University of Ministry of Education of China (No.NCET-11-0674);National Natural Science Foundation of China (No.11372167);Supported by the Natural Science Basis Research Plan in Shaanxi Province of China (No.2012JQ8051);Yulin Science and Technology Project in 2013 (No.sf13-43)
- DOI：10.3969/j.issn.0372-2112.2014.09.021
  CLC： TN713
- Published：2014
- 稿件说明：
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ZHANG Yu-mei, WU Xiao-jun, BAI Shu-lin. A DFP-Based Second-Order Volterra Model with Applications to Rössler Chaotic Series Prediction[J]. Acta Electronica Sinica, 2014, 42(9): 1801-1806.
DOI：

ZHANG Yu-mei, WU Xiao-jun, BAI Shu-lin. A DFP-Based Second-Order Volterra Model with Applications to Rössler Chaotic Series Prediction[J]. Acta Electronica Sinica, 2014, 42(9): 1801-1806. DOI： 10.3969/j.issn.0372-2112.2014.09.021.

摘要

为克服最小二乘法或归一化最小二乘法在二阶Volterra建模时参数选择不当引起的问题，在最小二乘法基础上，应用一种基于后验误差假设的可变收敛因子技术，构建了一种基于Davidon-Fletcher-Powell算法的二阶Volterra模型（DFPSOVF）.给出参数估计中自相关逆矩阵估计的递归更新公式，并对其正定性、有界性和

（

）的作用进行了研究.将DFPSOVF模型应用于Rssler混沌序列的单步预测，仿真结果表明其能够保证算法的稳定性和收敛性，不存在最小二乘法和归一化最小二乘法的发散问题.

Abstract

To overcome problems caused by improper parameters selection when least mean square(LMS)or normalized LMS(NLMS)method is apphied to model second-order Volterra

based on the LMS method

a novel Davidon-Fletcher-Powell(DFP)-method-based second-order Volterra filter(DFPSOVF)was proposed

which is based on a posteriori error assumption and is characteristic of a variable convergence factor.Recursive update formulation positive definiteness and bounded property of the inverse autocorrelation matrix estimation

role of

(

) of DFPSOVF model are studied.Simulations

which apply DFPSOVF model to single step predictions for Rssler chaotic time series

illustrate that the proposed algorithm can guarantee

its stability and convergence and there haven't divergence problems using LMS and NLMS algorithms.

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