Semi-deterministic finite interval estimation of linear system dynamics and output trajectory
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
An efficient approach adopting Reproducing Kernel Hilbert Space, RKHS, to estimate the parameters of Differential Equations from noisy realizations of the system's output is presented in this thesis.Initially, this thesis studies the previous works on parameter and state estimation using RKHS.This approach estimates the parameters, order n, the output trajectory and the derivatives of the system up to n-1, where n is the true order.The presented approach is able to handle error in the variable using local fitting and regularization.The suggested method uses Bayesian Information Criterion, BIC, to evaluate possible order for unknown systems.Lastly, to increase the accuracy and computational speed, the approach applies hyper-parameter search and cross-validation to tune its cost function's coefficients.The MATLAB software package has been implemented to evaluate the suggested approach.i List of Figures 1 Feedback Controller with State Estimator . . . . . . . . . . . . . . . . . . 2 A closed loop control system with state estimator [74] . . . . . . . . . . . . 3 Noisy y M vs. nominal y for noise of 1 SD (SNR -8.9652 dB) . . . . . . . . 4 Estimate of y using second order vs. nominal y for noise of 1 SD (SNR -8.9652 dB) . . . . . . . . . . . . . . .
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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