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Stochastic behavior of the nonnegative least mean fourth algorithm for stationary Gaussian inputs and slow learning

机译:平稳高斯输入和慢学习的非负最小均值第四算法的随机行为

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摘要

Some system identification problems impose nonnegativity constraints on the parameters to be estimated due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow one to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the stochastic behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis.
机译:由于未知系统的固有物理特性,一些系统识别问题对要估计的参数施加了非负约束。非负最小均方(NNLMS)算法及其变体允许人们以在线方式解决此问题。最近,提出了一种非负最小均值第四算法(NNLMF),以在测量噪声不是高斯的情况下提高这些算法的性能。本文对固定高斯输入和慢速学习的NNLMF算法的随机行为进行了首次理论分析。仿真结果说明了所提出分析的准确性。

著录项

  • 来源
    《Signal processing》 |2016年第11期|18-27|共10页
  • 作者单位

    School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China;

    School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China;

    Center of Intelligent Acoustics and Immersive Communications (CIAIC), School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China;

    The Universite de Nice Sophia-Antipolis, UMR CNRS 7293, Observatoire de la Cote d'azur, Laboratoire Lagrange, Parc Valrose, 06102 Nice, France;

    The Department of Electrical Engineering, Federal University of Santa Catarina, 88040-900 Florianopolis, SC, Brazil;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Adaptive filter; Least mean fourth (LMF); Mean weight behavior; Nonnegativity constraint; Second-order moment; System identification;

    机译:自适应滤波器最低均值第四(LMF);平均体重行为;非负约束二阶矩系统识别;

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