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

Yang, Liangdong; Liu, Jinxin; Yan, Ruqiang; Chen, Xuefeng

doi:10.1016/j.sigpro.2019.06.007

Cited by 40 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our proposed HSF and IHSF algorithms, there is no updated step-size formula compared to the VSS-LHCAF [ 42 ] and VSS-Llncosh [ 45 ] algorithms, meaning that the computational complexity of HSF and IHSF algorithms is smaller than that of the VSS-LHCAF [ 42 ] and VSS-Llncosh[ 45 ] algorithms. There is also no normalized required to compute, as is the case with the adaptive filtering algorithm with arctangent cost [ 48 ]. The HSF and IHSF algorithms also reduce the number of parameters that need to be set.…”

Section: Proposed Algorithm Based On the Hyperbolic Sine Functionmentioning

confidence: 99%

“…And hyperbolic tangent function was also constructed in a drought disaster losses model, which was used to analyze the characteristics of a drought disaster losses curve with multiple inflection points and nonlinearity [ 40 ]. In addition, to reduce the interference of impulse noise on the traditional spline adaptive filter algorithm for identifying Wiener-type non-systems, an arctangent function that is insensitive to large outliers is used to construct a cost function, so that the spline adaptive filter is robustness to impulse noise [ 41 ]. Since it is difficult to obtain an analytical result of the expectation of the hyperbolic tangent function under the Gaussian assumption, the hyperbolic tangent-based robust filter and its performance analysis are not considered in both robust LMS and the switching algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Guan

Cheng²,

Zhao

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More importantly, the hyperbolic sine function has not been discussed. In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function. Specifically, development of a robust adaptive filtering algorithm based on HSF, and extend the HSF algorithm to another novel adaptive filtering algorithm based on IHSF; then continue to analyze the computational complexity for HSF and IHSF; finally, validation of the analyses and superiority of the proposed algorithm via simulations. The HSF and IHSF algorithms can attain superior steady-state performance and stronger robustness in impulsive interference than several existing algorithms for different system identification scenarios, under Gaussian noise and impulsive interference, demonstrate the superior performance achieved by HSF and IHSF over existing adaptive filtering algorithms with different hyperbolic functions.

show abstract

Section: Proposed Algorithm Based On the Hyperbolic Sine Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Guan

Cheng²,

Zhao

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Furthermore, to speed the convergence of the aforementioned sign algorithms, the variable step-size SAF-SNLMS and the variable step size sign subband adaptive filter (VSS-SSAF) [22] were introduced. In order to further mitigate the interference of impulsive noises in the identification of Wiener-type nonlinear systems, an enhanced SAF algorithm, named SAF-ARC-MMSGD, was proposed in [23]. The Geman-McClure estimator can significantly compress the data with large amplitude [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Robust Geman-McClure Based Nonlinear Spline Adaptive Filter Against Impulsive Noise

Liu

2020

IEEE Access

View full text Add to dashboard Cite

This paper first proposes nonlinear spline adaptive filter based on the robust Geman-McClure estimator (SAF-RGM). The proposed algorithm is obtained by minimizing the cost function relied on the Geman-McClure estimator. Since the Geman-McClure estimator can remove outliers with large amplitude from dataset, the proposed algorithm can obtain the excellent performance in the impulsive noise. Moreover, the mean and mean square behaviors of the SAF-RGM algorithm are analyzed. Simulations are conducted to confirm that the proposed SAF-RGM algorithm achieves better performance than the existing spline nonlinear adaptive filtering algorithms. Besides, simulation results validate the theoretical conclusions.

show abstract

“…It adds secondary vibration source to the control system and cancels the unwanted vibration based on the principle of superposition. [1][2][3][4][5][6][7] Generally, the aim of AVC system is to suppress the unwanted vibration as much as possible. However, with the development of the technology, there is a new branch called active vibration reshaping arising, 8,9 which may find application in fields of sound quality control, psychoacoustics, military camouflage, wearable device, etc.…”

Section: Introductionmentioning

confidence: 99%

Fast multiline spectral reshaping algorithm for active vibration control

Liu

Zhang

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

Self Cite

View full text Add to dashboard Cite

Multiline spectral vibration (or noise) is very common in rotating machinery like motor, gearbox, compressor, propeller, etc. Active reshaping of these vibrations is one of the most important branches for active vibration control, which may find application in fields of sound quality control, psychoacoustics, military camouflage, etc. The traditional filteredreference LMS is the most popular structure for multiline spectral reshaping control. However, with the increase of spectral lines, vibration sources and observation points (feedback channels), the traditional structure will have extremely heavy computational complexity, especially for the system whose dynamic of secondary path is complex, since it requires two long filters for every single spectral line and secondary path to adjust the reference signals. In this study, we propose a fast-multiline spectrum reshaping algorithm for active vibration control. We first construct an equivalent system of the traditional multiline spectrum reshaping algorithm by introducing two extra control branches, which is able to improve the convergence property of the system. Then, we generate a group of pre-phase-scheduled reference signals using the phase information at certain frequencies of the secondary paths instead of using filters. Afterwards, we conduct a theoretical analysis of computational complexity of multiline spectrum reshaping and fast multiline spectrum reshaping algorithms. Finally, we carried out two case studies based on the FEMilS test-bed to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm performs an accurate residual vibration control with uniformed convergence speed and reduced computational complexity.

show abstract

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

Cited by 40 publications

References 27 publications

Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Robust Geman-McClure Based Nonlinear Spline Adaptive Filter Against Impulsive Noise

Fast multiline spectral reshaping algorithm for active vibration control

Contact Info

Product

Resources

About