Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Optimal placement of loudspeakers and microphones in an enclosure using genetic algorithm
AU - Montazeri, A.
AU - Poshtan, J.
AU - Kahaei, M. H.
PY - 2003
Y1 - 2003
N2 - In this paper genetic algorithm has been used for optimal placement of loudspeakers and microphones in an experimental enclosure for. active noise control system. A control system with two secondary loudspeakers and four error microphones is used to reduce the potential energy generated by primary source in a frequency range of 50Hz to 300Hz. By choosing the average of potential energy in this frequency range as a performance index, genetic algorithm is used to find the global minimum of this function. This performance index is a function of the locations of microphones and loudspeakers, hence a configuration of them corresponds to a value for this performance index. In this genetic algorithm a multivariable binary coding scheme along with a random initial population are used. Computation of the potential energy, and also the acoustical pressure pattern in a specified level shows that the locations obtained by genetic algorithm are very effective in the results.
AB - In this paper genetic algorithm has been used for optimal placement of loudspeakers and microphones in an experimental enclosure for. active noise control system. A control system with two secondary loudspeakers and four error microphones is used to reduce the potential energy generated by primary source in a frequency range of 50Hz to 300Hz. By choosing the average of potential energy in this frequency range as a performance index, genetic algorithm is used to find the global minimum of this function. This performance index is a function of the locations of microphones and loudspeakers, hence a configuration of them corresponds to a value for this performance index. In this genetic algorithm a multivariable binary coding scheme along with a random initial population are used. Computation of the potential energy, and also the acoustical pressure pattern in a specified level shows that the locations obtained by genetic algorithm are very effective in the results.
KW - ACTUATOR LOCATIONS
KW - genetic algorithm
KW - optimal sensor placement
KW - ANC
KW - ACTIVE NOISE-CONTROL
KW - SIMULATION
KW - modal analysis
U2 - 10.1109/CCA.2003.1223278
DO - 10.1109/CCA.2003.1223278
M3 - Conference contribution/Paper
SN - 0-7803-7729-X
VL - 1
SP - 135
EP - 139
BT - Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on
PB - IEEE
CY - New York
T2 - IEEE Conference on Control Applications
Y2 - 23 June 2003 through 25 June 2003
ER -