Improved-Variable-Forgetting-Factor Recursive Algorithm Based on the Logarithmic Cost for Volterra System Identification

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 l...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Vol. 63; no. 6; pp. 588 - 592
Main Authors: Lu, Lu, Zhao, Haiquan, Chen, Badong
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive filters
Adaptive recursive algorithm
Algorithms
Convergence
Cost function
Estimation
Kernel
Noise
Nonlinear systems
Performance enhancement
Recursive
Robustness
Signal processing algorithms
System identification
Tracking
Variable forgetting factor (VFF)
Volterra system
α-stable noise
alpha-stable noise
Volterra system
adaptive recursive algorithm
Adaptive filters
variable forgetting factor (VFF)
ISSN:1549-7747, 1558-3791
Online Access:Get full text
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Summary: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.
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ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2016.2531159