Towards the Optimal Learning Rate for Backpropagation
Neural Processing Letters
Adaptive error-constrained method for LMS algorithms and applications
Signal Processing
Plant identification via adaptive combination of transversal filters
Signal Processing - Signal processing in UWB communications
A homomorphic neural network for modeling and prediction
Neural Computation
Variable step-size LMS algorithm with a quotient form
Signal Processing
Adaptive improved natural gradient algorithm for blind source separation
Neural Computation
Wireless Personal Communications: An International Journal
LMS algorithm with an adaptive neural network cost function
WSEAS TRANSACTIONS on COMMUNICATIONS
A variable step-size affine projection algorithm
Digital Signal Processing
A modified Armijo rule for the online selection of learning rate of the LMS algorithm
Digital Signal Processing
An interference-robust stochastic gradient algorithm with a gradient-adaptive step-size
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
Utilising variable vector step-size in normalised LMS algorithm
International Journal of Computer Applications in Technology
An effective method to improve convergence for sequential blind source separation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Nonlinear spline adaptive filtering
Signal Processing
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The step size of this adaptive filter is changed according to a gradient descent algorithm designed to reduce the squared estimation error during each iteration. An approximate analysis of the performance of the adaptive filter when its inputs are zero mean, white, and Gaussian noise and the set of optimal coefficients are time varying according to a random-walk model is presented. The algorithm has very good convergence speed and low steady-state misadjustment. The tracking performance of these algorithms in nonstationary environments is relatively insensitive to the choice of the parameters of the adaptive filter and is very close to the best possible performance of the least mean square (LMS) algorithm for a large range of values of the step size of the adaptation algorithm. Several simulation examples demonstrating the good properties of the adaptive filters as well as verifying the analytical results are also presented