Towards the Optimal Learning Rate for Backpropagation
Neural Processing Letters
A Modular Analog NLMS Structure for Adaptive Filtering
Analog Integrated Circuits and Signal Processing - Selected papers from the 1st Analog VLSI Workshop
Adaptive error-constrained method for LMS algorithms and applications
Signal Processing
On length adaptation for the least mean square adaptive filters
Signal Processing - Fractional calculus applications in signals and systems
Plant identification via adaptive combination of transversal filters
Signal Processing - Signal processing in UWB communications
Variable Step Size Affine Projection Algorithm for Adaptive Multiuser DS-CDMA MMSE Receiver
Wireless Personal Communications: An International Journal
A noise resilient variable step-size LMS algorithm
Signal Processing
An adaptive penalized maximum likelihood algorithm
Signal Processing
New gradient-based variable step size LMS algorithms
EURASIP Journal on Advances in Signal Processing
Variable step-size LMS algorithm with a quotient form
Signal Processing
Krylov-proportionate adaptive filtering techniques not limited to sparse systems
IEEE Transactions on Signal Processing
The quaternion LMS algorithm for adaptive filtering of hypercomplex processes
IEEE Transactions on Signal Processing
A new approach to active noise and vibration control-part I: the known frequency case
IEEE Transactions on Signal Processing
Wireless Personal Communications: An International Journal
LMS algorithm with an adaptive neural network cost function
WSEAS TRANSACTIONS on COMMUNICATIONS
A variable step-size LMS adaptive filtering algorithm
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
A variable step-size affine projection algorithm
Digital Signal Processing
Prediction-based watermarking schemes using ahead/post AC prediction
Signal Processing
Optimized digital automatic gain control for DVB-S2 system
WTS'10 Proceedings of the 9th conference on Wireless telecommunications symposium
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
A VLMS based pseudo-fractional optimum order estimation algorithm
Proceedings of the 2011 International Conference on Communication, Computing & Security
Utilising variable vector step-size in normalised LMS algorithm
International Journal of Computer Applications in Technology
An Easy Rake Receiver for TD-SCDMA Smart Antenna
Wireless Personal Communications: An International Journal
A variable step-size selective partial update LMS algorithm
Digital Signal Processing
Efficient Artifact Elimination in Cardiac Signals using Variable Step Size Adaptive Noise Cancellers
International Journal of Measurement Technologies and Instrumentation Engineering
KIMEL: A kernel incremental metalearning algorithm
Signal Processing
Multichannel self-optimizing narrowband interference canceller
Signal Processing
International Journal of Computational Vision and Robotics
Wireless Personal Communications: An International Journal
Hi-index | 35.69 |
A number of time-varying step-size algorithms have been proposed to enhance the performance of the conventional LMS algorithm. Experimentation with these algorithms indicates that their performance is highly sensitive to the noise disturbance. This paper presents a robust variable step-size LMS-type algorithm providing fast convergence at early stages of adaptation while ensuring small final misadjustment. The performance of the algorithm is not affected by existing uncorrelated noise disturbances. An approximate analysis of convergence and steady-state performance for zero-mean stationary Gaussian inputs and for nonstationary optimal weight vector is provided. Simulation results comparing the proposed algorithm to current variable step-size algorithms clearly indicate its superior performance for cases of stationary environments. For nonstationary environments, our algorithm performs as well as other variable step-size algorithms in providing performance equivalent to that of the regular LMS algorithm