Deep Neural Network-based Black-box Modeling of Power Electronic Converters Using Transfer Learning
Bibliographic record
Abstract
Black-box modeling of power electronic converters (PECs) is an essential tool for studying commercial converters. Neural-networks-based modeling techniques are data-driven based solely on signal measurements. Recent studies show that Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) structures used in deep neural network (DNN) black-box modeling methods are capable of modeling steady-state and transient behavior of PECs. Training times for such structures can be extremely long. This paper uses a technique called “transfer-learning” to improve the training times of DNN-based black-box modeling techniques based on LSTM and GRU. The models are trained using different data sizes to study the effects of transfer learning thoroughly. The detailed switching models of two synchronous DC/DC buck converters” as well as two HPUC converters” are simulated in MATLAB/Simulink environment for gathering training and test data. The obtained results demonstrate the transfer-learning method's superiority over conventional techniques in terms of improved training times.
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How this classification was reachedexpand
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".