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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".