Online Unified Solution for Selective Harmonic Elimination Based on Stochastic Configuration Network and Levenberg–Marquardt Algorithm
Bibliographic record
Abstract
In this article, an online unified solution based on stochastic configuration network (SCN) and Levenberg– Marquardt (LM) algorithm is proposed to generate optimal switching angles for selective harmonic elimination (SHE) in both the symmetric and asymmetric cascaded H-bridge (CHB) multilevel inverters (MLIs). Different from the traditional neural network framework for SHE, this unified solution uses SCN to only generate initial values of the switching angles online, which significantly reduce the precision demand on training SCN and avoids the local optima problem of gradient descent algorithm. After obtaining the initial values from SCN online, the LM algorithm can be used to rapidly solve the exact switching angles for SHE, which guarantees the solving efficiency and precision of the final solutions. Moreover, the stability of the proposed method is proved via Newton-like convergence theory. Compared with intelligent search algorithms and look-up table method, the proposed solution can not only online generate optimal theoretical switching angles but with much fewer data storage space. Experimental results of both symmetric and asymmetric MLI cases are presented to validate the correctness and online applicability of the proposed solution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".