Bibliographic record
Abstract
Model order selection for linear time-invariant (LTI) systems is an important system modeling concern and has been widely investigated through past decades. Different approaches of order selection such as Akaike information criterion (AIC), Bayesian information criterion (BIC), minimum description length (MDL) and reconstruction error LTI system identification (RE-LTI) propose different criteria to select the optimum order of a system. In many real life applications of model order selection the size of an observed data set is increasing. Thus, order selection methods need to adopt the best fit of a model as the data set size is increasing. This is our motivation to extend RE-LTI order selection for online application of order selection with lower computational cost and complexity. It has been shown previously that AIC, BIC, two-stage MDL and many existing order selection criteria are special cases of RE-LTI method. Our online order selection approach reduces the computational complexity of the offline approach from O(N3) to O(N2). It should be noted that RE-LTI and MNDL order selection methods have same fundamentals and consequently extending RE-LTI to online RE-LTI also extends MNDL to online MNDL. Another crucial issue in system identification and modeling is estimating the time delay of a system’s impulse response (or determining the start of its non-zero part). This problem is addressed in various areas including radar, sonar, acoustic source tracking, multipath channel identification, as well as many automatic control applications. Utilizing fundamentals of RE-LTI approach, here we introduce a new time-delay estimator. Simulation results show advantages of the proposed method and its superiority to existing approaches in accuracy and robustness in terms of the FIT index.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".