Adaptive Neural Fuzzy Inference System Controller for Seven-Level Packed U-Cell Inverter
Bibliographic record
Abstract
In this paper, an improved Adaptive Neural Fuzzy Inference System (ANFIS) based controller has been designed to regulate the capacitor voltage and load current of Seven-Level Packed U-Cell (PUC7) inverter. PUC7 is an affordable topology due to the least power switches and DC sources; but, it suffers from unstable adjusting capacitor voltage level. Proportional Integral (PI) as a simple linear control method causes overshoots and undershoots in transient response and conducts the system to unstable mode when some uncertainties are existed in the load. However, the proposed ANFIS control method regulates the load current and balances the capacitor voltage without any overshoot or undershoot and transient response even in presence of nonlinear loads. Simulation results of stand-alone mode of operation of PUC7 obtained by MATLAB software also confirm usability of ANFIS control loop to achieve unity power factor, minimum Total Harmonic Distortion (THD) and minimum capacitor voltage ripple while the PUC7 inverter is connected to an AC dynamic load.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".