Sea Clutter Sequences Regression Prediction Based on PSO-GRNN Method
Why this work is in the frame
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Bibliographic record
Abstract
In marine radar signal processing, in order to suppress sea clutter, sea clutter sequences regression prediction is necessary. Sea clutter has chaotic features, and GRNN (General Regression Neural Network) algorithm can effectively predict regression of chaotic sequences, this paper presents a sea clutter sequences regression prediction method based on an improved GRNN algorithm, using phase space reconstruction to strike GRNN training samples, applying adaptive PSO (Particle Swarm Optimization) algorithm to optimize GRNN Gaussian width coefficient, then the IPIX radar data of Canada Mc Master University were used, to doing the experiment on sea clutter forecast. The results showed that: regression model to predict sea clutter is feasible, and PSO-GRNN method can higher improve the prediction accuracy than GRNN method.
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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 | 0.000 |
| 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