Component sizing of a plug-in hybrid electric vehicle powertrain, Part B: coupling bee-inspired metaheuristics to ensemble of local neuro-fuzzy radial basis identifiers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, the authors investigate the potentials of an aggregated cooperative intelligent approach to optimise the size of components of a plug-in hybrid electric vehicle (PHEV) powertrain. The intelligent model consists of a set of modular local neuro-fuzzy radial basis identifiers. These intelligent tools are finally incorporated to develop a global identifier called ensemble neuro-fuzzy radial basis network (ENFRBN). The resulted global identifier synchronously uses the local maps to predict the fuel consumption (FC) rate of a PHEV for a specific drive cycle. To do so, an experimental/simulative sampling process was performed in smart hybrid and electric vehicle system laboratory at the University of Waterloo to create a database including a set of input/output pairs. After extracting knowledge from prepared database, the authors use two well-known bee-inspired heuristic algorithms, i.e., bee algorithm (BA) and artificial bee colony (ABC) to reach a compromise on optimal size of PHEV components.
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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 | 0.001 | 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