Contactor Modeling Technology Based on an Artificial Neural Network
Bibliographic record
Abstract
We propose a new contactor modeling method that incorporates the back propagation (BP) neural network to map the complex nonlinear electromechanical coupling relation of the contactor to build its model. First, the artificial neural network (ANN) model collects the actual operational data of the contactor, including the coil voltage, coil current and moving core displacement, and then uses the strong nonlinear fitting ability of the BP neural network to perform the model training. When the training is completed, the ANN model can output the precise displacement according to the input data of the coil voltage and the coil current. Through a simple training process, this method can complete the modeling of any electromagnetic contactor. This method avoids the need to solve the complex magnetic circuit equation of the contactor and thus provides a simple and universal method for calculating the displacement of the electromagnetic switch. The co-simulation system is used to model, train, and analyze the contactor ANN model. Finally, a relevant experiment is conducted to confirm the effectiveness of the ANN model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".