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Active noise control using adaptive POLYnominal Gaussian WinOwed wavelet networks.

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Title: Active noise control using adaptive POLYnominal Gaussian WinOwed wavelet networks.
Authors: Akraminia, M.1 m.akrami@ut.ac.ir, Mahjoob, M. J.1, Tatari, M.1
Source: Journal of Vibration & Control. Nov2015, Vol. 21 Issue 15, p3020-3033. 14p.
Subjects: Active noise control, Gaussian distribution, Wavelets (Mathematics), Algorithms, Nonlinear analysis, Impulse response, Closed loop systems, Lyapunov functions
Abstract: The capabilities of wavelet networks in function approximation make them appealing for black box system identification. In this paper, a new active noise control (ANC) algorithm is developed based on adaptive wavelet networks. The proposed adaptive nonlinear noise control approach employs frames from POLYnominal WinOwed with Gaussian wavelets. Also, a novel network structure for active noise control is derived incorporating a nonlinear static mapping cascaded with an infinite impulse response filter to model the dynamic part of the network. Online dynamic backpropagation learning algorithms based on gradient descent method are applied to adjust the network parameters. Local convergence of the closed-loop system is proved using discrete Lyapunov function.The performance of the proposed ANC system is examined for typical linear/nonlinear cases. The simulation results demonstrate superior performance of this method in terms of stability, fast convergence rate and noise attenuation while avoiding curse of dimensionality. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Vibration & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: The capabilities of wavelet networks in function approximation make them appealing for black box system identification. In this paper, a new active noise control (ANC) algorithm is developed based on adaptive wavelet networks. The proposed adaptive nonlinear noise control approach employs frames from POLYnominal WinOwed with Gaussian wavelets. Also, a novel network structure for active noise control is derived incorporating a nonlinear static mapping cascaded with an infinite impulse response filter to model the dynamic part of the network. Online dynamic backpropagation learning algorithms based on gradient descent method are applied to adjust the network parameters. Local convergence of the closed-loop system is proved using discrete Lyapunov function.The performance of the proposed ANC system is examined for typical linear/nonlinear cases. The simulation results demonstrate superior performance of this method in terms of stability, fast convergence rate and noise attenuation while avoiding curse of dimensionality. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Vibration & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/1077546313520025
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 3020
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      – SubjectFull: Active noise control
        Type: general
      – SubjectFull: Gaussian distribution
        Type: general
      – SubjectFull: Wavelets (Mathematics)
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Nonlinear analysis
        Type: general
      – SubjectFull: Impulse response
        Type: general
      – SubjectFull: Closed loop systems
        Type: general
      – SubjectFull: Lyapunov functions
        Type: general
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      – TitleFull: Active noise control using adaptive POLYnominal Gaussian WinOwed wavelet networks.
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            NameFull: Akraminia, M.
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            NameFull: Mahjoob, M. J.
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            NameFull: Tatari, M.
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            – D: 01
              M: 11
              Text: Nov2015
              Type: published
              Y: 2015
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