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Record W2134254077 · doi:10.1109/ccece.2008.4564682

Implementation of the RBF neural network on a SOPC for maximum power point tracking

2008· article· en· W2134254077 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMaximum power point trackingPID controllerPulse-width modulationDuty cycleField-programmable gate arrayComputer scienceArtificial neural networkPhotovoltaic systemGate arrayMaximum power principleController (irrigation)Programmable logic controllerElectronic engineeringEngineeringControl theory (sociology)Control engineeringEmbedded systemElectrical engineeringVoltageArtificial intelligenceControl (management)Temperature control

Abstract

fetched live from OpenAlex

In this paper, a radial basis function (RBF) neural network is implemented as a system on a programmable chip (SOPC) to carry out maximum power point tracking (MPPT) for photovoltaic (PV) control systems. The implementation of the SOPC can provide a traditional proportional integral derivative (PID) controller and some additional hardware like a pulse width modulation (PWM) generator by a general purpose field programmable gate array (FPGA) chip, as well as integrate a RBF neural network controller by embedded soft processors. The tracking algorithm changes the duty-cycle of the IGBTs to make the PV converter work at maximum power. As a result, the MPPT unit of the PV system becomes an independent and highly integrated product including peripheral design and a control algorithm.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.019
GPT teacher head0.220
Teacher spread0.201 · 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