A class of stochastic gradient algorithms with exponentiated error cost functions
Digital Signal Processing
Split quaternion nonlinear adaptive filtering
Neural Networks
A modified Armijo rule for the online selection of learning rate of the LMS algorithm
Digital Signal Processing
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The least mean squares (LMS) is the most widely used algorithm among those proposed to adapt the coefficients of an FIR filter in order to minimize the mean-square error (MSE) between its output and the desired signal. Since the introduction of the LMS algorithm, many variants have been proposed to improve its performance. Doubtless, the most popular is the normalized LMS algorithm, which uses a value for the adaptation constant that assures the fastest convergence. This correspondence shows a new demonstration of the algorithm based on a mathematical approach easier than that usually proposed