A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control
Active noise control (ANC) represents an efficient technology for enhancing the noise suppression performance and ensuring the stable operation of multi-sensor systems through generative model-enhanced data representation and dynamic information fusion across heterogeneous sensors due to the complex...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/8/2569 |
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| author | Thi Trung Tin Nguyen Faxiang Zhang Jing Na Le Thai Nguyen Gengen Li Altyib Abdallah Mahmoud Ahmed |
| author_facet | Thi Trung Tin Nguyen Faxiang Zhang Jing Na Le Thai Nguyen Gengen Li Altyib Abdallah Mahmoud Ahmed |
| author_sort | Thi Trung Tin Nguyen |
| collection | DOAJ |
| description | Active noise control (ANC) represents an efficient technology for enhancing the noise suppression performance and ensuring the stable operation of multi-sensor systems through generative model-enhanced data representation and dynamic information fusion across heterogeneous sensors due to the complexity of the real-world environment. To address problems caused by a nonlinear noise source, a novel adaptive neuro-fuzzy network controller is proposed for feedforward nonlinear ANC systems based on a variable step-size filtered-x least-mean-square (VSS-LMS) algorithm. Specifically, the LMS algorithm is first introduced to update the weight parameters of the controller based on the adaptive neuro-fuzzy network. Then, a variable step-size adjustment strategy is proposed to calculate the learning gain used in the LMS algorithm, which aims to improve the nonlinear noise suppression performance. Additionally, the stability of the proposed method is proven by the discrete Lyapunov theorem. Extensive simulation experiments show that the proposed method surpasses the mainstream ANC methods with regard to nonlinear noise. |
| format | Article |
| id | doaj-art-2160b88c59684a998f30eaa59cacdeb5 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2160b88c59684a998f30eaa59cacdeb52025-08-20T03:13:45ZengMDPI AGSensors1424-82202025-04-01258256910.3390/s25082569A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise ControlThi Trung Tin Nguyen0Faxiang Zhang1Jing Na2Le Thai Nguyen3Gengen Li4Altyib Abdallah Mahmoud Ahmed5Yunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, ChinaYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, ChinaYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, ChinaFaculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City 700000, VietnamYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, ChinaYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, ChinaActive noise control (ANC) represents an efficient technology for enhancing the noise suppression performance and ensuring the stable operation of multi-sensor systems through generative model-enhanced data representation and dynamic information fusion across heterogeneous sensors due to the complexity of the real-world environment. To address problems caused by a nonlinear noise source, a novel adaptive neuro-fuzzy network controller is proposed for feedforward nonlinear ANC systems based on a variable step-size filtered-x least-mean-square (VSS-LMS) algorithm. Specifically, the LMS algorithm is first introduced to update the weight parameters of the controller based on the adaptive neuro-fuzzy network. Then, a variable step-size adjustment strategy is proposed to calculate the learning gain used in the LMS algorithm, which aims to improve the nonlinear noise suppression performance. Additionally, the stability of the proposed method is proven by the discrete Lyapunov theorem. Extensive simulation experiments show that the proposed method surpasses the mainstream ANC methods with regard to nonlinear noise.https://www.mdpi.com/1424-8220/25/8/2569active noise controlfiltered-x least-mean-square algorithmvariable step-size learningadaptive neuro-fuzzy networknonlinear path |
| spellingShingle | Thi Trung Tin Nguyen Faxiang Zhang Jing Na Le Thai Nguyen Gengen Li Altyib Abdallah Mahmoud Ahmed A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control Sensors active noise control filtered-x least-mean-square algorithm variable step-size learning adaptive neuro-fuzzy network nonlinear path |
| title | A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control |
| title_full | A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control |
| title_fullStr | A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control |
| title_full_unstemmed | A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control |
| title_short | A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control |
| title_sort | variable step size fxlms algorithm for nonlinear feedforward active noise control |
| topic | active noise control filtered-x least-mean-square algorithm variable step-size learning adaptive neuro-fuzzy network nonlinear path |
| url | https://www.mdpi.com/1424-8220/25/8/2569 |
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