Performance evaluation of EKF-based chaotic synchronization
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Bibliographic record
Abstract
The performance of chaotic synchronization based on the extended Kalman filter (EKF) is investigated here. We first establish the relationship between the EKF-based synchronization method and two conventional synchronization method, drive-response and unidirectionally coupled methods. The performance of the EKF-based synchronization method in the presence of channel noise is then derived in terms of mean square error (MSE) between the drive and response systems for one-dimensional discrete-time systems. Compared with the optimal coupled synchronization method, the EKF-based synchronization method is shown to have the same MSE performance for chaotic systems with gradient square independent of the system states (Type-I systems). For chaotic systems with state-dependent gradient square (Type-II systems), the EKF-based method is found to have a smaller MSE. The averaged Cramer-Rao lower bound (CRLB) is introduced here as a performance measure. It is shown that the EKF-based method approaches the averaged CRLB for both Type-I and Type-II systems when noise level is low. Our theoretical results are verified by using Monte Carlo simulation on three popular one-dimensional chaotic systems.
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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 | 0.001 | 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