Reconstruction of piecewise chaotic dynamics using a multiple model approach
Bibliographic record
Abstract
In this paper we propose using a multiple model (MM) predictor to reconstruct piecewise chaotic dynamic. The motivation relies on the observation that conventional single model is usually incapable of reconstructing the piecewise dynamic properly because a piecewise map is non-differentiable. In our approach, multiple radial basis function (RBF) neural nets are used to model the dynamic in different partition intervals. Switching between different intervals could be estimated by a nonlinear gate model. In particular, an Expectation-Maximization (EM) algorithm is employed to train the MM-RBF. Compared to the conventional approach, the proposed MM is shown to greatly improve the reconstruction performance for piecewise chaotic dynamic. We further apply it to combat channel distortions in an analog chaotic spread spectrum (SS) system. It is found that the proposed method has satisfactory equalization performance even when channel effect is strong.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".