Blind System Identification Using Symbolic Dynamics
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Bibliographic record
Abstract
In this paper, a chaos-based approach is proposed for system identification with binary random signal. A chaos-based approach is developed to model random binary sequence and is applied to blind system identification. The Cramér Rao Lower Bound (CRLB)-based on the chaos representation is derived. The theoretical mean square error of the proposed approach is also derived. It is shown that the proposed blind approach achieves the CRLB asymptotically. The proposed technique is applied to blind channel equalization of a quadrature amplitude modulation communication system. The equalizer is based on expected maximization and unscented Kalman filtering and smoother. Our proposed method shows superior performance in comparison with conventional blind equalization techniques. The significance of this research is to extend the advantages of chaos to random signals for the blind system identification.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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