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.CorrectIncorrect -
Question 2 of 5
2. Question
The LMS algorithm always converges faster as
increases.CorrectIncorrect -
Question 3 of 5
3. Question
If
![{\bf x}[n]](https://allsignalprocessing.com/wp-content/plugins/latex/cache/tex_9e8dabd5636be83b5a2429cf5dcd1ebc.gif)
denotes the data applied to the weights at time 
, which of the following is a reasonable bound on to ensure the LMS iteration converges?CorrectIncorrect -
Question 4 of 5
4. Question
Misadjustment refers to the impact of using the noisy instantaneous gradient when the weights are near the optimum.
CorrectIncorrect -
Question 5 of 5
5. Question
Which of the following properties are always improved by decreasing the size of
? Select all that apply.CorrectIncorrect
