Bibliographic record
Abstract
The modelling of photovoltaic (PV) solar cells using a hybrid adaptive neuro‐fuzzy inference system (ANFIS) algorithm is presented. It is based on the decomposition of the cell output current into photocurrent and junction current. The photocurrent is linearly dependent on solar irradiance and cell temperature; consequently, its analytical computation is done easily. However, the junction current is highly non‐linear and depends on cell voltage and temperature. Therefore, its analytical computation is complicated and the manufacturers do not supply any information about this parameter. Moreover, there is no way to measure it physically. Therefore, it is proposed to use the ANFIS algorithm as a powerful technique in order to estimate this current and reconstruct the output PV cell current using the photocurrent. The model validation is based on the gradient descent and chain rule applied to a set of data different than the one used for training process. The advantage of the proposed model is that only one climatic parameter is used as the input to the ANFIS algorithm, which makes it less sensitive to climatic variations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".