Adaptive signal processing
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Adaptive filters with error nonlinearities: mean-square analysis and optimum design
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
A time-domain feedback analysis of filtered-error adaptive gradientalgorithms
IEEE Transactions on Signal Processing
Exploiting sparsity in adaptive filters
IEEE Transactions on Signal Processing
A unified approach to the steady-state and tracking analyses ofadaptive filters
IEEE Transactions on Signal Processing
Transient analysis of adaptive filters with error nonlinearities
IEEE Transactions on Signal Processing
IEEE Transactions on Education
The least mean fourth (LMF) adaptive algorithm and its family
IEEE Transactions on Information Theory
Digital Signal Processing
Adaptive mixture methods based on Bregman divergences
Digital Signal Processing
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A novel class of stochastic gradient descent algorithms is introduced based on the minimisation of convex cost functions with exponential dependence on the adaptation error, instead of the conventional linear combinations of even moments. The derivation is supported by rigourous analysis of the necessary conditions for convergence, the steady state mean square error is calculated and the optimal solutions in the least exponential sense are derived. The normalisation of the associated step size is also considered in order to fully exploit the dynamics of the input signal. Simulation results support the analysis.