Strategies for Non-Linear System Identification in Base Excited Structure
Bibliographic record
Abstract
The traditional nonlinear parameter identification techniques described in the existing literature required force and response information at all degrees of freedom. For cases, where the excitation comes from base input, those methods cannot be applied directly unless the measurement is made in all degrees of freedom. The emphasis of this research is upon nonlinear identification of the large, multi-mode, lightly damped, continuous system, where the excitation comes from the moving base. The emphasis is to identify the nonlinear model in parametric forms, where the linear modal testing method is not sufficient to describe the dynamics of the structure. For this reason, a method suitable for the identification of a model based on improved hybrid modal space and modal space is considered. The proposed methodology shows the extraction process of pseudo force projected at the measured degrees of freedom. An experiment is performed to validate the proposed method in a cantilever beam. Nonlinear parameter selections are done through multilinear regression in a modal domain. A significant cubic stiffness nonlinearity is found in the first mode. The cross-coupling stiffness terms are found to be insignificant during regression analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".