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Record W2015528486 · doi:10.1021/ie020956d

Feedforward and Feedback Controller Performance Assessment of Linear Time-Variant Processes

2003· article· en· W2015528486 on OpenAlexafffund
Folake Olaleye, Biao Huang, Edgar C. Tamayo

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2003
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsSyncrude (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSyncrudeAlexander von Humboldt-Stiftung
KeywordsFeed forwardBenchmark (surveying)Control theory (sociology)Computer scienceFeedback controlController (irrigation)Feedback controllerControl (management)Variance (accounting)Feedforward neural networkControl engineeringEngineeringArtificial intelligenceArtificial neural network

Abstract

fetched live from OpenAlex

This paper is concerned with the feedforward and feedback control performance assessment of linear time-variant processes. The developed algorithms provide a way to calculate the time-varying minimum-variance feedforward and feedback control benchmark from routine operating data. The proposed techniques are illustrated through a simulated stirred-tank heater example and an industrial application.

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 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.035
GPT teacher head0.290
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations23
Published2003
Admission routes2
Has abstractyes

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