Convergence, Tracking, and the LMS Algorithm Step Size
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Question 1 of 5
1. Question
The LMS algorithm step-size parameter
may be chosen by the user to achieve different performance tradeoffs.
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Question 2 of 5
2. Question
The LMS algorithm always converges faster as
increases.
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Question 3 of 5
3. Question
If
denotes the data applied to the weights at time
, which of the following is a reasonable bound on
to ensure the LMS iteration converges?
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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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Question 5 of 5
5. Question
Which of the following properties are always improved by decreasing the size of
? Select all that apply.
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