TY - JOUR
T1 - Improved-Variable-Forgetting-Factor Recursive Algorithm Based on the Logarithmic Cost for Volterra System Identification
AU - Lu, Lu
AU - Zhao, Haiquan
AU - Chen, Badong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6
Y1 - 2016/6
N2 - Compared with the least-mean-square algorithm, the least mean pth power algorithm shows a better robustness performance against impulsive noises such as the α-stable noises. However, it still exhibits slow convergence rate and high kernel misadjustment. To overcome this drawback, a novel recursive logarithmic least mean pth (RLLMP) algorithm is proposed for the Volterra system identification under α-stable noise environments. Instead of minimizing the pth power, the new algorithm aims to minimize the pth logarithmic cost, which makes it more robust against impulsive interferences. Furthermore, to enhance tracking performance, an improved variable forgetting factor (IVFF) algorithm (IVFF-RLLMP) is proposed, which is based on the robust estimation of outliers. Simulation results are presented to demonstrate the improved performance of the RLLMP and IVFF-RLLMP.
AB - Compared with the least-mean-square algorithm, the least mean pth power algorithm shows a better robustness performance against impulsive noises such as the α-stable noises. However, it still exhibits slow convergence rate and high kernel misadjustment. To overcome this drawback, a novel recursive logarithmic least mean pth (RLLMP) algorithm is proposed for the Volterra system identification under α-stable noise environments. Instead of minimizing the pth power, the new algorithm aims to minimize the pth logarithmic cost, which makes it more robust against impulsive interferences. Furthermore, to enhance tracking performance, an improved variable forgetting factor (IVFF) algorithm (IVFF-RLLMP) is proposed, which is based on the robust estimation of outliers. Simulation results are presented to demonstrate the improved performance of the RLLMP and IVFF-RLLMP.
KW - Adaptive filters
KW - Volterra system
KW - adaptive recursive algorithm
KW - variable forgetting factor (VFF)
KW - α-stable noise
UR - https://www.scopus.com/pages/publications/84973364266
U2 - 10.1109/TCSII.2016.2531159
DO - 10.1109/TCSII.2016.2531159
M3 - 文章
AN - SCOPUS:84973364266
SN - 1549-7747
VL - 63
SP - 588
EP - 592
JO - IEEE Transactions on Circuits and Systems II: Express Briefs
JF - IEEE Transactions on Circuits and Systems II: Express Briefs
IS - 6
M1 - 7410007
ER -