Stability of adaptive systems: passivity and averaging analysis
Stability of adaptive systems: passivity and averaging analysis
Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
Comparison of convex combination and affine combination of adaptive filters
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A performance-weighted mixture of LMS filters
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
On combinations of CMA equalizers
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Adaptive combination of proportionate filters for sparse echo cancellation
IEEE Transactions on Audio, Speech, and Language Processing
Improving the Tracking Capability of Adaptive Filters via Convex Combination
IEEE Transactions on Signal Processing - Part II
A feedback approach to the steady-state performance of fractionallyspaced blind adaptive equalizers
IEEE Transactions on Signal Processing
Universal linear prediction by model order weighting
IEEE Transactions on Signal Processing
Global convergence of fractionally spaced Godard (CMA) adaptiveequalizers
IEEE Transactions on Signal Processing
A unified approach to the steady-state and tracking analyses ofadaptive filters
IEEE Transactions on Signal Processing
Mean-square performance of a convex combination of two adaptive filters
IEEE Transactions on Signal Processing
An Affine Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis
IEEE Transactions on Signal Processing
Stochastic Stability Analysis for the Constant-Modulus Algorithm
IEEE Transactions on Signal Processing - Part I
A gradient search interpretation of the super-exponential algorithm
IEEE Transactions on Information Theory
Adaptive combination of subband adaptive filters with selective partial updates
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Adaptive mixture methods based on Bregman divergences
Digital Signal Processing
| Hi-index | 35.68 |
In this paper, we propose an approach to the transient and steady-state analysis of the affine combination of one fast and one slow adaptive filters. The theoretical models are based on expressions for the excess mean-square error (EMSE) and cross-EMSE of the component filters, which allows their application to different combinations of algorithms, such as least mean-squares (LMS), normalized LMS (NLMS), and constant modulus algorithm (CMA), considering white or colored inputs and stationary or nonstationary environments. Since the desired universal behavior of the combination depends on the correct estimation of the mixing parameter at every instant, its adaptation is also taken into account in the transient analysis. Furthermore, we propose normalized algorithms for the adaptation of the mixing parameter that exhibit good performance. Good agreement between analysis and simulation results is always observed.