Energy Conservation and the Learning Ability of LMS Adaptive Filters
نویسندگان
چکیده
This chapter provides an overview of interesting phenomena pertaining to the learning capabilities of stochastic-gradient adaptive filters, and in particular those of the least-mean-squares (LMS) algorithm. The phenomena indicate that the learning behavior of adaptive filters is more sophisticated, and also more favorable, than was previously thought, especially for larger step-sizes. The discussion relies on energy conservation arguments and elaborates on both the mean-square convergence and the almost-sure convergence behavior of an adaptive filter.
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