A nonlinear prediction approach for system identification using chaos symbolic dynamic
Bibliographic record
Abstract
In this paper we propose using a nonlinear prediction approach to identify an autoregressive (AR) system with chaos symbolic driven signal. This problem widely exists in many practical situations such as channel equalization for chaos digital communications. Although statistic-based techniques can be used to identify systems driven by chaos symbolic signals, they may not fully exploit the information contained in a deterministic chaos symbolic signal and may not result in an optimal solution. In fact, the nonlinear dynamic of a chaos symbolic signal could be properly approximated by using a radial basis function (RBF) net. Based on the short-term predictability of a chaos symbolic signal, an efficient inverse filtering identification approach is proposed. More precisely, a nonlinear prediction error criterion is used as an objective function in the inverse filtering blind identification method. Compared to the statistically optimal least square (LS) method, the proposed nonlinear predictive method is shown to greatly improve the AR system identification performance. We further apply it to combat channel distortions in a digital chaos communication 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 | 0.001 |
| 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".