Estimation for SISO LTI systems using differential invariance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A two-step non-asymptotic approach for parameter and state estimation in Reproducing Kernel Hilbert Space (RKHS) is presented in this thesis.It begins with the understanding and derivation of double sided kernel representation for a fourth order linear system and proceeds into discussing and developing methods for state and parameter estimation from single noisy realizations of the system output on a time interval [a, b].Once the parameters are estimated the output is reconstructed by projection onto the span of fundamental solutions and this in turn is used to reconstruct the time derivatives of the system output. List ofFigures iv 5.14 True and reconstructed second derivative of the system output with AWGN of = 0 and = 1 and N=15000 . . . . . . . . . . . . . . . . . . . . . . .5.15 True and reconstructed third derivative of the system output with AWGN of = 0 and = 1 and N=15000 . . . . . . . . . . . . . . . . . . . . . . .5.16 True and reconstructed output trajectories of the system with AWGN of = 0 and = 1 and sample size N = 600 . . . . . . . . . . . . . . . . . .5.17 True and reconstructed output trajectories of the system with AWGN of = 0 and = 1 and sample size N = 6000 . . . . . . . . . . . . . . . . . .5.18 True and reconstructed output trajectories of the system with AWGN of = 0 and = 1 and sample size N = 15000 . . . . . . . . . . . . . . . . .5.19 True and noisy system output with AWGN of = 0 and = 2 and N=12000 5.20 True and reconstructed output trajectories of the system with AWGN of = 0 and = 2 and sample size N = 12000 . . . . . . . . . . . . . . . . .5.21 True and reconstructed first derivative of the system output with AWGN of = 0 and = 2 and N=12000 . . . . . . . . . . . . . . . . . . . . . . . .5.22 True and reconstructed second derivative of the system output with AWGN of = 0 and = 2 and N=12000 . . . . . . . . . . . . . . .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it