TY - JOUR
T1 - Fast convergent genetic search for adaptive IIR filtering
AU - Ng, S. C.
AU - Chung, C. Y.
AU - Leung, S. H.
AU - Luk, Andrew
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 1994
Y1 - 1994
N2 - The classical learning algorithms for adaptive IIR filtering, such as Gradient-descent algorithm and Least Square Techniques, suffer from several weaknesses. First, the convergence time is too long even for low order filters. Second, the algorithms fail to converge to the global optimum when the error function is multimodal. To tackle the above difficulties, a new learning algorithm for adaptive IIR filtering is proposed. In this paper, the genetic search is introduced into the gradient-descent algorithm, such as the Least-Mean-Square (LMS) algorithm, so as to provide global search capability and to further improve its convergence speed. In addition, the new algorithm is also applied to lattice structure of IIR filters for providing a more stable behavior.
AB - The classical learning algorithms for adaptive IIR filtering, such as Gradient-descent algorithm and Least Square Techniques, suffer from several weaknesses. First, the convergence time is too long even for low order filters. Second, the algorithms fail to converge to the global optimum when the error function is multimodal. To tackle the above difficulties, a new learning algorithm for adaptive IIR filtering is proposed. In this paper, the genetic search is introduced into the gradient-descent algorithm, such as the Least-Mean-Square (LMS) algorithm, so as to provide global search capability and to further improve its convergence speed. In addition, the new algorithm is also applied to lattice structure of IIR filters for providing a more stable behavior.
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U2 - 10.1109/ICASSP.1994.390079
DO - 10.1109/ICASSP.1994.390079
M3 - RGC 21 - Publication in refereed journal
SN - 1520-6149
VL - 3
SP - 105
EP - 108
JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
M1 - 390079
T2 - Proceedings of the 1994 IEEE International Conference on Acoustics, Speech and Signal Processing. Part 2 (of 6)
Y2 - 19 April 1994 through 22 April 1994
ER -