Chaotic system reconstruction from noisy time series measurements using improved least squares genetic programming
Why this work is in the frame
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Bibliographic record
Abstract
The problem of chaotic system reconstruction in the presence of measurement noise is not only an important one from the viewpoint of communication systems and radar signal processing, but also a challenging one if one has no a priori knowledge of the system structure. In this paper, we propose a novel algorithm based on genetic programming to reconstruct not only the structure of the underlying chaotic dynamical system but also the optimal parameters of the dynamical system using time series measurements that are corrupted by additive Gaussian noise. We show via computer simulations that the proposed algorithm called improved least squares genetic program (ILS-GP) is able to reconstruct different kinds of chaotic systems from their noisy time series measurements even at low SNRs. We finally show the improved ability of the ILS-GP algorithm by applying it to predict the time series of airborne radar sea clutter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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