Adaptive signal processing
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Adaptive system identification and signal processing algorithms
Adaptive system identification and signal processing algorithms
Adaptive system identification and signal processing algorithms
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Adaptive echo cancellation using least mean mixed-norm algorithm
IEEE Transactions on Signal Processing
An error-entropy minimization algorithm for supervised training ofnonlinear adaptive systems
IEEE Transactions on Signal Processing
A variable step size LMS algorithm
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
A novel kurtosis driven variable step-size adaptive algorithm
IEEE Transactions on Signal Processing
The least mean fourth (LMF) adaptive algorithm and its family
IEEE Transactions on Information Theory
Generalized information potential criterion for adaptive system training
IEEE Transactions on Neural Networks
Advanced search algorithms for information-theoretic learning with kernel-based estimators
IEEE Transactions on Neural Networks
A noise constrained least mean fourth (NCLMF) adaptive algorithm
Signal Processing
Mean-square convergence analysis of ADALINE training with minimum error entropy criterion
IEEE Transactions on Neural Networks
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In this paper, we propose a minimum-error entropy with self-adjusting step -size (MEE-SAS) as an alternative to the minimum-error entropy (MEE) algorithm for training adaptive systems. MEE-SAS has faster speed of convergence as compared to MEE algorithm for the same misadjustment. We attribute the self-adjusting step-size property of MEE-SAS to its changing curvature as opposed to MEE which has a constant curvature. Analysis of the curvature shows that MEE-SAS converges faster in noisy scenarios than noise-free scenario, thus making it more suitable for practical applications as shown in our simulations. Finally, in case of non-stationary environment, MEE-SAS loses its tracking ability due to the ''flatness'' of the curvature near the optimal solution. We overcome this problem by proposing a switching scheme between MEE and MEE-SAS algorithms for non-stationary scenario, which effectively combines the speed of MEE-SAS when far from the optimal solution with the tracking ability of MEE when near the solution. We demonstrate the performance of the switching algorithm in system identification in non-stationary environment.