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Entropy
Volume 26
Issue 12
10.3390/e26121010
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Communication

Reduced Gaussian Kernel Filtered-x LMS Algorithm with Historical Error Correction for Nonlinear Active Noise Control

by
Jinhua Ku
1,2,
Hongyu Han
1,2,*,
Weixi Zhou
1,2,
Hong Wang
1,2 and
Sheng Zhang
3
1
College of Computer Science, Sichuan Normal University, Chengdu 610101, China
2
Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu 610066, China
3
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
*
Author to whom correspondence should be addressed.
Entropy 2024, 26(12), 1010; https://doi.org/10.3390/e26121010
Submission received: 3 November 2024 / Revised: 19 November 2024 / Accepted: 21 November 2024 / Published: 22 November 2024
(This article belongs to the Section Multidisciplinary Applications)
Versions Notes

Abstract

This paper introduces a reduced Gaussian kernel filtered-x least mean square (RGKxLMS) algorithm for a nonlinear active noise control (NANC) system. This algorithm addresses the computational and storage challenges posed by the traditional kernel (i.e., KFxLMS) algorithm. Then, we analyze the mean weight behavior and computational complexity of the RGKxLMS, demonstrating its reduced complexity compared to existing kernel filtering methods and its mean stable performance. To further enhance noise reduction, we also develop the historical error correction RGKxLMS (HECRGKxLMS) algorithm, incorporating historical error information. Finally, the effectiveness of the proposed algorithms is validated, using Lorenz chaotic noise, non-stationary noise environments, and factory noise.
Keywords: nonlinear active noise control; kernel filtered-x least mean square algorithm; error-correction learning; nonlinearity issues nonlinear active noise control; kernel filtered-x least mean square algorithm; error-correction learning; nonlinearity issues

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MDPI and ACS Style

Ku, J.; Han, H.; Zhou, W.; Wang, H.; Zhang, S. Reduced Gaussian Kernel Filtered-x LMS Algorithm with Historical Error Correction for Nonlinear Active Noise Control. Entropy 2024, 26, 1010. https://doi.org/10.3390/e26121010

AMA Style

Ku J, Han H, Zhou W, Wang H, Zhang S. Reduced Gaussian Kernel Filtered-x LMS Algorithm with Historical Error Correction for Nonlinear Active Noise Control. Entropy. 2024; 26(12):1010. https://doi.org/10.3390/e26121010

Chicago/Turabian Style

Ku, Jinhua, Hongyu Han, Weixi Zhou, Hong Wang, and Sheng Zhang. 2024. "Reduced Gaussian Kernel Filtered-x LMS Algorithm with Historical Error Correction for Nonlinear Active Noise Control" Entropy 26, no. 12: 1010. https://doi.org/10.3390/e26121010

APA Style

Ku, J., Han, H., Zhou, W., Wang, H., & Zhang, S. (2024). Reduced Gaussian Kernel Filtered-x LMS Algorithm with Historical Error Correction for Nonlinear Active Noise Control. Entropy, 26(12), 1010. https://doi.org/10.3390/e26121010

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