Adaptive error-constrained method for LMS algorithms and applications
Signal Processing
A noise resilient variable step-size LMS algorithm
Signal Processing
An adaptive penalized maximum likelihood algorithm
Signal Processing
Variable step-size LMS algorithm with a quotient form
Signal Processing
WSEAS Transactions on Circuits and Systems
Prediction-based watermarking schemes using ahead/post AC prediction
Signal Processing
An online AUC formulation for binary classification
Pattern Recognition
On the design of LMS-based channel estimators using the Doppler spread parameter
Digital Signal Processing
A variable step-size selective partial update LMS algorithm
Digital Signal Processing
KIMEL: A kernel incremental metalearning algorithm
Signal Processing
| Hi-index | 35.68 |
A least-mean-square (LMS) adaptive filter with a variable step size is introduced. The step size increases or decreases as the mean-square error increases or decreases, allowing the adaptive filter to track changes in the system as well as produce a small steady state error. The convergence and steady-state behavior of the algorithm are analyzed. The results reduce to well-known results when specialized to the constant-step-size case. Simulation results are presented to support the analysis and to compare the performance of the algorithm with the usual LMS algorithm and another variable-step-size algorithm. They show that its performance compares favorably with these existing algorithms