Development and implementation of a simplified self-tuned neuro-fuzzy based IM drive
Bibliographic record
Abstract
In this paper a novel and simplified self-tuned neuro-fuzzy controller (NFC) is developed for speed control of an induction motor (IM) drive. The proposed NFC combines fuzzy logic and a four-layer artificial neural network (ANN) scheme. The proposed control scheme decreases the computational burden as compared to the conventional NFC without sacrificing the performance. In the proposed NFC only speed error is employed as input. The simple structure of the proposed NFC makes it easier to be implemented in practical applications. Based on the knowledge of vector control and back propagation (BP) algorithm an unsupervised self-tuning method is developed to adjust membership functions and weights of the proposed NFC. The complete drive incorporating the proposed self tuned NFC is experimentally implemented using a digital signal processor board DS-1104 for a laboratory 1/3 hp motor. The effectiveness of the proposed NFC based vector control of IM drive is tested both in simulation and experiment at different operating conditions. Comparison of results in simulation and experiment proves that the simplification of the proposed NFC does not decrease the system performance as compared to conventional NFC.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".