A bilinear functional link artificial neural network filter for nonlinear active noise control and its stability condition

Le, Dinh Cong; Zhang, Jiashu; Pang, Yanjie

doi:10.1016/j.apacoust.2017.10.023

Cited by 37 publications

(12 citation statements)

References 29 publications

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“…e multilayer neural networks have been proposed for nonlinear ANC systems [29][30][31][32]. Halimeh et al also introduced a neural networkbased approach to the problem of nonlinear acoustic echo cancellation (NLAEC) [33].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Huynh

Chang

2021

Mathematical Problems in Engineering

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Noise in a dynamic system is practically unavoidable. Today, such noise is commonly reduced using an active noise control (ANC) system with the filtered-x least mean square (FXLMS) algorithm. However, the performance of the ANC system with FXLMS algorithm is significantly impaired in nonlinear systems. Therefore, this paper develops an efficient nonlinear adaptive feedback neural controller (NAFNC) to eliminate narrowband noise for both linear and nonlinear ANC systems. The proposed controller is implemented to update its coefficients without prior offline training by neural network. Hence, the proposed method has rapid convergence rate as confirmed by simulation results. The proposed work also analyzes the stability and convergence of the proposed algorithm. Simulation results verify the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Huynh

Chang

2021

Mathematical Problems in Engineering

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“…To compensate for the strong nonlinearity of the system with saturated signals, several bilinear algorithms with reduced computational complexity were proposed, which employ the cross-terms based on both feedback and feedforward polynomials and they can model NLANC systems with shorter filter length [36,37]. In [38], a new bilinear filter was proposed, which uses FLANN to expand the input vector and then adapts coefficients via bilinear filter to obtain excellent nonlinear modeling capability.…”

Section: Introductionmentioning

confidence: 99%

A survey on active noise control in the past decade–Part II: Nonlinear systems

Yin

Lamare

et al. 2021

Signal Processing

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“…One way is to use the recursive structure, which can be seen as an expansion of the infinite impulse response (IIR) filter. This kind of NANC algorithm includes the bilinear filter algorithm [28,29], recursive second-order Volterra filter [30], recursive even mirror Fourier nonlinear filter [31,32], adaptive function expansion bilinear filter [33,34], and so on. However, it is worth noting that the stability of all recursive algorithms mentioned above should be carefully considered, and more efforts must be made to make the recursive algorithm stable [35].…”

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition

et al. 2021

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Nonlinear active control is very important in many practical applications. Many well-known nonlinear active noise control algorithms may suffer from high computational complexity and low convergence speed, especially in the nonlinear secondary path case. Thus, it is still an actively researched topic for reducing complexity and improving the convergence rate. This paper presents a low-complexity Volterra filtered-error least mean square algorithm when taking a decomposable Volterra model into account for active control of nonlinear noise processes, which is referred as DVMFELMS. The computational complexity analysis shows that the proposed DVMFELMS algorithm can significantly reduce the nonlinear active noise control system’s complexity. The simulation results further show that the proposed algorithm can achieve promising performance compared with the Volterra-based FELMS algorithm and other state-of-the-art nonlinear filters, while the decomposable error of the Volterra kernel may be introduced inevitably. Moreover, the proposed DVMFELMS algorithm shows a better convergence rate in the broadband primary noise case due to fewer parameters used in each sub-filter.

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A bilinear functional link artificial neural network filter for nonlinear active noise control and its stability condition

Cited by 37 publications

References 29 publications

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

A survey on active noise control in the past decade–Part II: Nonlinear systems

A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition

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