Adaptive neuro-fuzzy inference system-based controllers for smart material actuator modelling
Why this work is in the frame
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Bibliographic record
Abstract
An intelligent approach for smart material actuator modelling of the actuation lines in a morphing wing system is presented, based on adaptive neuro-fuzzy inference systems. Four independent neuro-fuzzy controllers are created from the experimental data using a hybrid method — a combination of back propagation and least-mean-square methods — to train the fuzzy inference systems. The controllers' objective is to correlate each set of forces and electrical currents applied on the smart material actuator to the actuator's elongation. The actuator experi-mental testing is performed for five force cases, using a variable electrical current. An integrated controller is created from four neuro-fuzzy controllers, developed with Matlab/Simulink software for electrical current increases, constant electrical current, electrical current decreases, and for null electrical current in the cooling phase of the actuator, and is then validated by comparison with the experimentally obtained data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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