Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule
In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2009-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.3233/SAV-2009-0472 |
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| _version_ | 1849400405691203584 |
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| author | Ya-li Zhou Qi-zhi Zhang Tao Zhang Xiao-dong Li Woon-seng Gan |
| author_facet | Ya-li Zhou Qi-zhi Zhang Tao Zhang Xiao-dong Li Woon-seng Gan |
| author_sort | Ya-li Zhou |
| collection | DOAJ |
| description | In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system. |
| format | Article |
| id | doaj-art-87ca055ae79c427fbc23d034b3b54687 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-87ca055ae79c427fbc23d034b3b546872025-08-20T03:38:05ZengWileyShock and Vibration1070-96221875-92032009-01-0116332533410.3233/SAV-2009-0472Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning RuleYa-li Zhou0Qi-zhi Zhang1Tao Zhang2Xiao-dong Li3Woon-seng Gan4Department of Computer Science and Automation, Beijing Institute of Machinery, P.O. Box 2865, Beijing, 100192, ChinaDepartment of Computer Science and Automation, Beijing Institute of Machinery, P.O. Box 2865, Beijing, 100192, ChinaDepartment of Computer Science and Automation, Beijing Institute of Machinery, P.O. Box 2865, Beijing, 100192, ChinaInstitute of Acoustics, Academia Sinica, ChinaSchool of EEE, Nanyang Technological University, SingaporeIn practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.http://dx.doi.org/10.3233/SAV-2009-0472 |
| spellingShingle | Ya-li Zhou Qi-zhi Zhang Tao Zhang Xiao-dong Li Woon-seng Gan Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule Shock and Vibration |
| title | Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule |
| title_full | Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule |
| title_fullStr | Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule |
| title_full_unstemmed | Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule |
| title_short | Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule |
| title_sort | active noise control using a functional link artificial neural network with the simultaneous perturbation learning rule |
| url | http://dx.doi.org/10.3233/SAV-2009-0472 |
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