Statistical efficiency of adaptive algorithms
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Adaptive filters with error nonlinearities: mean-square analysis and optimum design
EURASIP Journal on Applied Signal Processing
Adaptive stepsize selection for tracking in a regime-switching environment
Automatica (Journal of IFAC)
HOS-based variable step adaptive equalizer
ISCGAV'04 Proceedings of the 4th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
A class of stochastic gradient algorithms with exponentiated error cost functions
Digital Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Adaptive fuzzy filtering in a deterministic setting
IEEE Transactions on Fuzzy Systems
Diffusion LMS strategies for distributed estimation
IEEE Transactions on Signal Processing
Journal of Signal Processing Systems
Performance analysis of the consensus-based distributed LMS algorithm
EURASIP Journal on Advances in Signal Processing
Distributed estimation over an adaptive incremental network based on the affine projection algorithm
IEEE Transactions on Signal Processing
Transient and steady-state analysis of the affine combination of two adaptive filters
IEEE Transactions on Signal Processing
Mean-square convergence analysis of ADALINE training with minimum error entropy criterion
IEEE Transactions on Neural Networks
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in theory and methods for nonstationary signal analysis
Mean square convergence analysis for kernel least mean square algorithm
Signal Processing
Computers and Electrical Engineering
A class of quaternion valued affine projection algorithms
Signal Processing
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Most adaptive filters are inherently nonlinear and time-variant systems. The nonlinearities in the update equations tend to lead to difficulties in the study of their steady-state performance as a limiting case of their transient performance. This paper develops a unified approach to the steady-state and tracking analyses of adaptive algorithms that bypasses many of these difficulties. The approach is based on the study of the energy flow through each iteration of an adaptive filter, and it relies on a fundamental error variance relation