Structure Identification of Filtered Fractionally Differenced Gaussian Noise Processes

肖创柏; 李衍达; 罗晖

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Structure Identification of Filtered Fractionally Differenced Gaussian Noise Processes

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- Structure Identification of Filtered Fractionally Differenced Gaussian Noise Processes
- Acta Electronica Sinica Issue 4, (1996)
- 作者机构：
  
  清华大学自动化系
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.7
- Published：1996
- 稿件说明：
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[1]肖创柏,李衍达,罗晖.滤波分形差分高斯噪声过程的结构辨识[J].电子学报,1996(04):29-34.

肖创柏, 李衍达, 罗晖. Structure Identification of Filtered Fractionally Differenced Gaussian Noise Processes[J]. Acta Electronica Sinica, 1996, (4).
[1]肖创柏,李衍达,罗晖.滤波分形差分高斯噪声过程的结构辨识[J].电子学报,1996(04):29-34. DOI：

肖创柏, 李衍达, 罗晖. Structure Identification of Filtered Fractionally Differenced Gaussian Noise Processes[J]. Acta Electronica Sinica, 1996, (4). DOI：

摘要

分形差分高斯噪声过程是分形布朗运动的一种离散等价情形，它的滤波形式非常适合于描述具有不同的短期间和长期间相关特性的信号。本文研究了滤波分形差分高斯噪声过程的结构辨识问题，针对存在较弱和适中的长期间相关的信号，提出了一种基于超定辅助变量乘积矩和最小描述长度准则的ＯＩＶＰＭ－ＭＤＬ结构辨识方法；针对存在强长期间相关的过程，提出了分形反滤波与ＯＩＶＰＭ－ＭＤＬ的组合定阶方法。方法的可行性与有效性通过大量的数值仿真试验得到验证

Abstract

Fractionally differenced Gaussian noise（fdGn） process is a discrete time equivalent of fractional Brownian motion.Filtered versions of such processes are ideally suited for modeling signals with different short-term and long-term correlation characteristics.In this paper

we study the structure identification of filtered fdGn processes.For signals that exhibit a weak or moderate long-term persistence

an OIVPM-MDL structure identification method is proposed based on overdetermined instrumental variable product moment（OIVPM） and minimum description length（MDL） criteria

and for signals with a strong long-term correlation between samples a combinative approach of inverse fractional filtering and the OIVPM-MDL method is also described.Examples are given to illustrate the feasibility and effectiveness of the proposed method.

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