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Record W2142517004 · doi:10.1109/ccece.1999.808186

Fuzzy logic controller for a DC to DC converter

2003· article· en· W2142517004 on OpenAlexaff
M. Smyej, A. Chériti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsFuzzy logicFlyback converterComputer scienceBuck converterBuck–boost converterForward converterTopology (electrical circuits)Electronic engineeringControl theory (sociology)Boost converterController (irrigation)MATLABLogic gatePower (physics)VoltageElectrical engineeringEngineeringControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

This paper describes in detail power converter control using fuzzy logic (FL). The converter consists of the buck topology. The advantage of this approach is simplicity as no linearized models of the power converter are required. The buck topology is tested and its output voltage is regulated using fuzzy logic. Simulation is done in the MATLAB-SIMULINK environment and an EPROM-based-digital-circuit is used for experiments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.022
GPT teacher head0.237
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2003
Admission routes1
Has abstractyes

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