Robust generalized hyperbolic secant algorithm for nonlinear active noise control

doi:10.1016/j.apacoust.2023.109422

2023-06-01

PRE

周杨 (Yang Zhou)

Haiquan Zhao *

Yingying Zhu

DOI：10.1016/j.apacoust.2023.109422

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摘要

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In nonlinear active noise control (ANC) system where the primary noise obeys non-Gaussian distribution, the traditional filter-s least mean square (FsLMS) algorithm is hardly satisfactory. In order to surmount this deficiency, a novel generalized hyperbolic secant function with lp norm (FsGHSFP) algorithm is pro-posed for nonlinear ANC system, which designs a robust optimization criterion utilizing the generalized hyperbolic secant distribution function. The FsGHSFP algorithm has robust noise attenuation capability for non-Gaussian noise, but suffers from the problem of tuning the parameter. To address this problem, an improved FsGHSFP (IFsGHSFP) algorithm is developed by taking advantage of the fact that continuous mixed norm does not need to adjust norm parameter. Moreover, in order to strike a balance between con-vergence and misalignment, thereby further enhancing the robustness of the ANC system, a convex com-bination algorithm of IFsGHSFP is suggested. The robustness mechanism of the proposed algorithms is analyzed, as well as stability conditions and computational complexity. It is verified by simulations that the proposed algorithms accelerate the convergence speed and improve the noise attenuation perfor-mance. In addition, the experimental results further validate the effectiveness of the proposed algorithms.(c) 2023 Elsevier Ltd. All rights reserved.

Keyword:

Active noise control

Generalized hyperbolic secant distribution

Functional link artificial neural network

Continuous mixed norm

Impulsive noise

Convex combination