Accelerating the convergence of adaptive filters for active noise control using particle swarm optimization

Monteiro, Rodrigo P.; Lima, Gabriel Alves de; Oliveira, Jose Paulo G. de; Cunha, D. S. C.; Bastos-Filho, Carmelo J. A.

doi:10.1109/la-cci.2017.8285676

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Each particle adjusts its position and speed according to the best solution obtained by the whole particle swarm and the best solution found separately. Because of its high efficiency, PSO is widely used in acoustics [31], electronics [32], reverse engineering [29], and other fields [33,34].…”

Section: Optimization Algorithm For Mpsm Jammingmentioning

confidence: 99%

Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Jiang

Wang

et al. 2019

Electronics

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Due to the difficulty in deducing the corresponding relationship between results and parameter settings of multiple phases sectionalized modulation (MPSM) jamming, a problem occurs when obtaining the optimal local suppression jamming effect, which limits the practical application of MPSM jamming. The traditional method struggles to meet the requirements by setting fixed parameters or random parameters. Therefore, an optimization algorithm for MPSM jamming based on particle swarm optimization (PSO) is proposed in this study to produce the optimal local suppression jamming effect and determine its corresponding parameter settings. First, we analyzed the relationship between the degree of mismatch and local suppression jamming effect. Then, we set appropriate fitness function and fitness value. Finally, we used PSO to calculate parameter settings of a section situation and phase situation, which minimizes the fitness function and fitness value. The optimization algorithm avoids the tremendous computation of traversing all parameter settings, is stable, the results are repeatable, and the algorithm provides the optimal local suppression jamming effect under different conditions. The simulation experiments demonstrate the feasibility and effectiveness of the optimization algorithm.

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Section: Optimization Algorithm For Mpsm Jammingmentioning

confidence: 99%

Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Jiang

Wang

et al. 2019

Electronics

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Robust Adaptive Estimator Using Evolutional Algorithm for Noise Cancellation in Multichannel System

Reddy¹,

Priya²,

Prasanth³

et al. 2019

Lecture Notes in Electrical Engineering

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Improving Adaptive Filters for Active Noise Control Using Particle Swarm Optimization

Monteiro

Lima

Oliveira³

et al. 2018

International Journal of Swarm Intelligence Research

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The excessive exposure to certain kinds of acoustic noise can lead to health problems. To avoid this situation, the use of noise attenuation devices is a standard solution. Among those devices, the active noise control (ANC) systems have gained prominence over the years, mainly due to the technological development and costs reduction of electronic components. Despite good performance of ANC concerning low-frequency noise attenuation, the convergence speed for this kind of system is still an important issue when it deals with real-time applications in dynamic environments. This article presents an alternative solution to accelerate the active attenuation system response. This solution is based on the use of sets of coefficients, which are employed during the adaptive filter initialization and are obtained via a training process with particle swarm optimization (PSO). Two objective functions were tested: one based on the response time itself and the other one based on the magnitude reduction of the residual noise. The coefficients obtained through this process provided response time reductions up to 98.3% concerning adaptive filters initialized with null coefficients. The article is an extended version of the conference paper Accelerating the Convergence of Adaptive Filters for Active Noise Control Using Particle Swarm Optimization, published in LA-CCI 2017.

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Accelerating the convergence of adaptive filters for active noise control using particle swarm optimization

Cited by 3 publications

References 12 publications

Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Optimization Algorithm for Multiple Phases Sectionalized Modulation Jamming Based on Particle Swarm Optimization

Robust Adaptive Estimator Using Evolutional Algorithm for Noise Cancellation in Multichannel System

Improving Adaptive Filters for Active Noise Control Using Particle Swarm Optimization

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