A Third-Order Tensor Decomposition Based Linear-In-The-Parameters Nonlinear Adaptive Filter
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Bibliographic record
Abstract
Linear-in-the-parameters (LIP) nonlinear adaptive filters are widely used for modelling nonlinear systems. However, one major draw-back of LIP nonlinear filters is the need to use high expansion orders to achieve accurate modelling, leading to a large number of inactive coefficients, hence overfitting issues and slow convergence. To overcome these issues, in this paper, we propose a third-order tensor (TOT) decomposition based functional link network (FLN) and propose the TOT-decomposition based FLN recursive least-squares algorithm (FLN-RLS-TOT). The decomposition technique significantly reduces the number of adaptive estimation parameters, thus allowing the use of a high expansion order to achieve accurate modelling while avoiding overfitting and convergence issues. Simulation results for nonlinear system identification show improved tracking, with similar or better modelling accuracy compared to existing algorithms.
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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