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Enhanced nonlinear active noise control: A novel approach using brain storm optimization algorithm.

Jiangchun Xie1, Jianmin Ma1

  • 1Department of Aeronautics and Astronauts, Fudan University, Shanghai, 200433, China.

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|August 21, 2024
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Summary
This summary is machine-generated.

This study introduces a novel Brain Storm Optimization (BSO) algorithm for Active Noise Control (ANC) systems, significantly improving nonlinear noise reduction. The BSO algorithm offers faster convergence and superior performance compared to traditional methods.

Keywords:
Active noise controlBrain storm optimization (BSO) algorithmFiltered-x least mean squares (FxLMS) algorithmMulti-frequency noiseNoise reduction performanceNonlinear noise reduction extended Kalman Filter (EKF)

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Area of Science:

  • Signal Processing
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Traditional Active Noise Control (ANC) methods, such as Filtered-x Least Mean Squares (FxLMS), are limited in handling nonlinear and complex noise patterns.
  • Existing ANC systems often struggle with adaptability and efficiency in dynamic acoustic environments.

Purpose of the Study:

  • To introduce a novel approach for nonlinear Active Noise Control (ANC) systems using the Brain Storm Optimization (BSO) algorithm.
  • To enhance the speed and accuracy of noise reduction in ANC systems, particularly for complex and nonlinear noise signals.
  • To compare the performance of the proposed BSO-based ANC system against conventional FxLMS algorithms.

Main Methods:

  • Implementation of the Brain Storm Optimization (BSO) algorithm, inspired by human brainstorming, incorporating principles like delayed evaluation and free imagination.
  • Integration of the BSO algorithm with an Extended Kalman Filter (EKF) to create an adaptive ANC system.
  • Experimental evaluation of the BSO-based ANC system's performance in reducing sinusoidal, multi-frequency, and random noise.

Main Results:

  • The BSO algorithm achieved up to 48 dB noise reduction for sinusoidal noise with a convergence time of 0.01 s.
  • In complex noise environments, the BSO algorithm reduced noise levels by up to 27 dB within 0.001 s.
  • The BSO-based ANC system demonstrated superior noise reduction and faster convergence compared to the FxLMS algorithm.

Conclusions:

  • The novel BSO algorithm significantly enhances the capabilities of ANC systems, especially for nonlinear and complex noise.
  • The proposed BSO-EKF approach offers improved adaptability, speed, and accuracy in noise control.
  • This research paves the way for advanced ANC technologies capable of handling a wider range of acoustic challenges.