ACTIVE NOISE CONTROL OF ACOUSTIC SOURCES USING SPHERICAL HARMONICS EXPANSION AND A GENETIC ALGORITHM: SIMULATION AND EXPERIMENT

1998 ◽  
Vol 212 (3) ◽  
pp. 511-523 ◽  
Author(s):  
T. Martin ◽  
A. Roure
Keyword(s):  
Genetic Algorithm ◽  
Spherical Harmonics ◽  
Noise Control ◽  
Active Noise Control ◽  
Active Noise ◽  
Simulation And Experiment ◽  
Spherical Harmonics Expansion ◽  
Acoustic Sources

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

2009 ◽  
Vol 57 (3) ◽  
pp. 221 ◽  
Author(s):  
Connor R. Duke ◽  
Scott D. Sommerfeldt ◽  
Kent L. Gee ◽  
Cole V. Duke
Keyword(s):  
Genetic Algorithm ◽  
Noise Control ◽  
Free Field ◽  
Active Noise Control ◽  
Active Noise ◽  
Control Source

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

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.

Application of the genetic algorithm to real-time active noise control

1996 ◽  
Vol 11 (3) ◽  
pp. 289-302 ◽  
Author(s):  
K. S. Tang ◽  
K. F. Man ◽  
S. Kwong ◽  
C. Y. Chan ◽  
C. Y. Chu
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Noise Control ◽  
Active Noise Control ◽  
Active Noise
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