Optimization of control source locations in free-field active noise control using a genetic algorithm

This paper presents the development of a neuro-adaptive active noise control (ANC) system. Multi-layered perceptron neural networks with a backpropagation learning algorithm are considered in both the modelling and control contexts. The capabilities of the neural network in modelling dynamical systems are investigated. A feedforward ANC structure is considered for optimum cancellation of broadband noise in a three-dimensional propagation medium. An on-line adaptation and training mechanism allowing a neural network architecture to characterise the optimal controller within the ANC system is developed. The neuro-adaptive ANC algorithm thus developed is implemented within a free-field environment and simulation results verifying its performance are presented and discussed.

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Experimental Design Methodology for Global Active Noise Control in Enclosed Space

10.21203/rs.3.rs-200272/v1 ◽

2021 ◽

Author(s):

Ikchae Jeong ◽

Youngjin Park

Keyword(s):

Experimental Design ◽

Field Condition ◽

Design Methodology ◽

Noise Control ◽

Noise Source ◽

Free Field ◽

Active Noise Control ◽

Active Noise ◽

Enclosed Space ◽

Experimental Design Methodology

Abstract The purpose of this paper is to propose an experimental design methodology for global active noise control in an enclosed space. We aim to control the noise caused by an internal noise source. Since each enclosed space has different acoustic characteristics, it is difficult to design different controllers suitable for each enclosed space. So, we decided to design a controller that could be used universally. The basic concept is the collocation of noise source and control speakers to generate a sound field opposite in phase to the noise source in a free field. For implementation of the proposed method, we propose a configuration method of control speakers and error microphones, and an active noise control algorithm. Also, to confirm the applicability of the proposed method, we design a controller in an anechoic chamber, which represents a free field condition, and perform active noise control in other enclosed spaces with the controller designed for the anechoic chamber. The experimental results show that the solution calculated in the free field condition can be used in other enclosed spaces without any modifications.

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A simple genetic algorithm for active noise control

The Journal of the Acoustical Society of America ◽

10.1121/1.422253 ◽

1998 ◽

Vol 103 (5) ◽

pp. 2779-2779

Author(s):

Antonio Minguez ◽

Manuel Recuero

Keyword(s):

Genetic Algorithm ◽

Noise Control ◽

Active Noise Control ◽

Active Noise ◽

Simple Genetic Algorithm

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Optimal parametric design of delayless subband active noise control system based on genetic algorithm optimization

Journal of Vibration and Control ◽

10.1177/10775463211001625 ◽

2021 ◽

pp. 107754632110016

Author(s):

Guo Long ◽

Yawen Wang ◽

Teik C Lim

Keyword(s):

Genetic Algorithm ◽

Control System ◽

Noise Control ◽

Parametric Design ◽

Active Noise Control ◽

Low Frequency ◽

Broadband Noise ◽

Algorithm Optimization ◽

Genetic Algorithm Optimization ◽

Active Noise

Active noise control systems are generally application-specific, and an appropriate algorithm with an optimal configuration is desirable in the first stage of active noise control system design and deployment. This study presents a design of the subband active noise control system with optimal parameters for a practical broadband active noise control. Although the delayless subband active noise control has gained wide attention for broadband noise cancellation, an optimal design remains a challenge because of the complex interplay between multiple factors such as spectral leakage, delay and weight stacking distortion subject to a number of configurable parameters, and weight stacking method. The configurable parameters can hardly be optimized independently because the active noise control performance depends on the combined configuration. A simple near black box active noise control algorithm optimization model is thus established by incorporating the genetic algorithm optimization into the parametric design of the delayless subband algorithm. The automated process does not require an understanding of the performance characteristics for different parameters. The significance of applying the automated genetic algorithm optimization to the complex multiparameter nonlinear active noise control design is revealed by numerical simulations, particularly for the multichannel low-frequency broadband active noise control system configured with the delayless subband algorithms. This provides a way for the optimal parametric design of subband active noise control before being used in a practical complex scenario.

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