Designed Excitation Biased Towards High Frequencies
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Quiet
- Tech stack
- python
- Domain
- data, machine-learning
Research direction
Start by obtaining the animation and the referenced dataset at /users/michael/sysID/data/srm_measurement_result_081226.npz, then reproduce the later excitation iterations for the 8th-order model. Compare the frequency coverage across iterations and identify why lower-frequency features are missed; done means the cause is documented and the excitation resolves the reported low-frequency behavior.
Written by the indexing model from the issue text.
Description
For a few realistic measurements I have conducted with a significant source of noise and a wide frequency range (about 2 decades) I have noticed that the designed excitation trends towards high frequency features during later iterations and misses or fails to resolve the lower frequency features. This is for a more complicated model to fit (8th order). I'll provide an animation to evaluate the evolution of the designed excitation over a few iterations as well as the data stored from this run.
I cannot upload my data here however it is stored on the controls computer at: /users/michael/sysID/data/srm_measurement_result_081226.npz
- Dominant language
- Python
- Stars
- 0
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
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