Convergence, Tracking, and the LMS Algorithm Step Size

The convergence and tracking behavior of the LMS algorithm are dependent on the step size parameter applied to the instantaneous gradient. The various performance tradeoffs involved with selecting a step size parameter are discussed. Small step sizes result in small misadjustment, but can have slow convergence and poor tracking performance. Large step sizes can result in unstable iterations.

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how much should the step size value be ideally, in terms of percentage within the given range of the step-size parameter?

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