Spline adaptive filter with arctangent-momentum strategy for nonlinear system identification

•An enhanced SAF is proposed.•The arctangent cost function is adopted to improve the robustness against the impulsive noises.•A modified momentum SGD is proposed to improve the convergence speed and reduce the steady-state error. In order to mitigate the interference of impulsive noises in the ident...

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Published inSignal processing Vol. 164; pp. 99 - 109
Main Authors Yang, Liangdong, Liu, Jinxin, Yan, Ruqiang, Chen, Xuefeng
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2019
Subjects
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ISSN0165-1684
1872-7557
DOI10.1016/j.sigpro.2019.06.007

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Abstract •An enhanced SAF is proposed.•The arctangent cost function is adopted to improve the robustness against the impulsive noises.•A modified momentum SGD is proposed to improve the convergence speed and reduce the steady-state error. In order to mitigate the interference of impulsive noises in the identification of Wiener-type nonlinear systems using traditional spline adaptive filter (SAF) algorithm, an enhanced SAF, named SAF-ARC-MMSGD, is proposed in this paper. Two improvements have been made in the proposed algorithm. First, the arctangent (ARC) function which is insensitive to large outliers is adopted to construct the cost function, so that the robustness of SAF against impulsive noises is improved. In addition, a modified momentum stochastic gradient descent (MMSGD) method containing two modifications, which are decay technique and selective strategy, is put forward in order to further improve the convergence speed and decrease the steady-state error during iteration. The convergence property of the proposed algorithm has been theoretically analyzed. The results of the numerical simulations have confirmed that the proposed algorithm has superior performance compared with the existing SAF related algorithms.
AbstractList •An enhanced SAF is proposed.•The arctangent cost function is adopted to improve the robustness against the impulsive noises.•A modified momentum SGD is proposed to improve the convergence speed and reduce the steady-state error. In order to mitigate the interference of impulsive noises in the identification of Wiener-type nonlinear systems using traditional spline adaptive filter (SAF) algorithm, an enhanced SAF, named SAF-ARC-MMSGD, is proposed in this paper. Two improvements have been made in the proposed algorithm. First, the arctangent (ARC) function which is insensitive to large outliers is adopted to construct the cost function, so that the robustness of SAF against impulsive noises is improved. In addition, a modified momentum stochastic gradient descent (MMSGD) method containing two modifications, which are decay technique and selective strategy, is put forward in order to further improve the convergence speed and decrease the steady-state error during iteration. The convergence property of the proposed algorithm has been theoretically analyzed. The results of the numerical simulations have confirmed that the proposed algorithm has superior performance compared with the existing SAF related algorithms.
Author Yang, Liangdong
Liu, Jinxin
Yan, Ruqiang
Chen, Xuefeng
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Keywords Wiener model
Momentum
Impulsive noise
Spline adaptive filter
Nonlinear system identification
Language English
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Snippet •An enhanced SAF is proposed.•The arctangent cost function is adopted to improve the robustness against the impulsive noises.•A modified momentum SGD is...
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SubjectTerms Impulsive noise
Momentum
Nonlinear system identification
Spline adaptive filter
Wiener model
Title Spline adaptive filter with arctangent-momentum strategy for nonlinear system identification
URI https://dx.doi.org/10.1016/j.sigpro.2019.06.007
Volume 164
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