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Convergence, Tracking, and the LMS Algorithm Step Size

May 9, 2019 by allsignal

Convergence, Tracking, and the LMS Algorithm Step Size

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  1. Question 1 of 5
    1. Question

    The LMS algorithm step-size parameter \mu may be chosen by the user to achieve different performance tradeoffs.

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  2. Question 2 of 5
    2. Question

    The LMS algorithm always converges faster as \mu increases.

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  3. Question 3 of 5
    3. Question

    If {\bf x}[n] denotes the data applied to the weights at time n, which of the following is a reasonable bound on \mu to ensure the LMS iteration converges?

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  4. Question 4 of 5
    4. Question

    Misadjustment refers to the impact of using the noisy instantaneous gradient when the weights are near the optimum.

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  5. Question 5 of 5
    5. Question

    Which of the following properties are always improved by decreasing the size of \mu? Select all that apply.

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