A new defuzzification method for fuzzy control of power converters
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a new defuzzification method is proposed which can provide improved performance in fuzzy control for DC-DC converters. A comparative study of different defuzzification methods adopted in fuzzy logic control (FLC), such as center of area (COA), center of sums (COS), height method (HM), middle of maxims (MOM), center of largest area (COLA), and first of maxims (FM), for application to DC-DC buck-converters is presented. The distinction among the characteristics which lead to varying performance is outlined. A new method called height weighted second maxims (HWSM) is proposed and its performance is assessed. The paper also presents simulation results of the performance of the closed-loop converters from the standpoint of start-up transient, bad regulation and line regulation. The simulations show that COA, COS, and HM defuzzification methods have better dynamic performance and less steady state error. The new HWSM defuzzification method provides further improvement.
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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