Artificial neural network based permanent magnet DC motor drives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A novel scheme for the speed control of a permanent magnet (PM) DC motor drive incorporating an artificial neural network (ANN) is proposed. The drive system includes an ANN speed controller, microprocessor based DC-DC power converter and a laboratory PM DC motor. A multilayer artificial neural network structure with a feedback loop is designed in order to precisely operate the control circuit for the DC-DC power converter. The complete drive system is simulated and implemented in real-time. Both the simulation and experimental results prove the inherent capability of the ANN which makes it possible to maintain desired speed control in the presence of parameter variations and load disturbances. The performances of the ANN based PM DC motor drive system are compared with the simulated results of the conventionally controlled drive system. This clearly indicates the better performance of the ANN based PM DC motor drive system, particularly in the case of parameter and load variations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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