Development of a neuro-fuzzy controller for induction motor
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Bibliographic record
Abstract
This paper presents a novel neuro-fuzzy (NF) controller based speed control scheme for a high performance induction motor (IM) drive. A 5-layer ANN structure is utilized to train the parameters of the FLC based on the minimization of the square of the error, which is considered as the object function. The FLC and ANN structures were developed based on motor control theory incorporating various uncertainties. A complete simulation model for indirect field oriented control of IM incorporating the proposed NF controller is developed. The performance of the proposed NF based IM drive is investigated at different operating conditions such as sinusoid reference speed, load disturbance and parameter disturbances. It is found that the performance of the proposed drive is robust and suitable for application in high performance industrial drive applications.
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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