TY - GEN
T1 - Convex Combination of Affine Projection and Error Coded Least Mean Square Algorithms
AU - Ibarra, Iker
AU - Rodriguez, Jocelyne
AU - Pichardo, Eduardo
AU - Avalos, Juan Gerardo
AU - Avalos, Guillermo
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11
Y1 - 2018/11
N2 - Affine Projection (AP) algorithms offer a relatively good convergence speed which can be increased by augmenting the projection order (L), however, in addition to presenting a high computational complexity, their steady-state misadjustment worsens in direct ratio to the rise of L. Convex combinations of AP algorithms have been devised in an attempt to address the misadjustment issue, albeit at the cost of doubling the aforementioned computational complexity. This work introduces the convex combination of an AP algorithm with an Error Coded Least Mean Square (ECLMS) algorithm, in order to reduce the twofold increase in computational complexity of dual AP combinations while retaining the high convergence speed and improving the steady-state misadjustment level. The proposed algorithm was tested in a system identification application, results demonstrate that the proposal performs as good or better than dual AP solutions, while considerably reducing computational complexity.
AB - Affine Projection (AP) algorithms offer a relatively good convergence speed which can be increased by augmenting the projection order (L), however, in addition to presenting a high computational complexity, their steady-state misadjustment worsens in direct ratio to the rise of L. Convex combinations of AP algorithms have been devised in an attempt to address the misadjustment issue, albeit at the cost of doubling the aforementioned computational complexity. This work introduces the convex combination of an AP algorithm with an Error Coded Least Mean Square (ECLMS) algorithm, in order to reduce the twofold increase in computational complexity of dual AP combinations while retaining the high convergence speed and improving the steady-state misadjustment level. The proposed algorithm was tested in a system identification application, results demonstrate that the proposal performs as good or better than dual AP solutions, while considerably reducing computational complexity.
KW - Adaptive filters
KW - Affine projection algorithm
KW - Convex combination
KW - Error coded least mean square
UR - http://www.scopus.com/inward/record.url?scp=85068835547&partnerID=8YFLogxK
U2 - 10.1109/ICMEAE.2018.00023
DO - 10.1109/ICMEAE.2018.00023
M3 - Contribución a la conferencia
AN - SCOPUS:85068835547
T3 - Proceedings - 2018 International Conference on Mechatronics, Electronics and Automotive Engineering, ICMEAE 2018
SP - 88
EP - 92
BT - Proceedings - 2018 International Conference on Mechatronics, Electronics and Automotive Engineering, ICMEAE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Mechatronics, Electronics and Automotive Engineering, ICMEAE 2018
Y2 - 27 November 2018 through 30 November 2018
ER -