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Record W2147632457 · doi:10.1109/cdc.1989.70542

A state space approach to minimum variance control of multivariable ARMAX systems

2003· article· en· W2147632457 on OpenAlexaff
C. Arnoldi, R.H. Kwong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultivariable calculusMinimum-variance unbiased estimatorMathematicsState spaceVariance (accounting)Control theory (sociology)Equivalence (formal languages)Algebraic Riccati equationState (computer science)Algebraic numberOptimal controlRiccati equationControl (management)Applied mathematicsComputer scienceMathematical optimizationStatisticsPure mathematicsAlgorithmMathematical analysisArtificial intelligenceEngineeringControl engineeringMean squared errorDifferential equation

Abstract

fetched live from OpenAlex

The minimum variance control problem for multivariable ARMAX systems is studied using a state-space approach. By reformulating it as a singular quadratic control problem, the stabilizing minimum variance control strategy is obtained by considering the stabilizing solution to the associated singular control algebraic Riccati equation. Various useful connections between the ARMAX and state-space representations of the systems are also found. These make it possible to establish equivalence of the state-space minimum variance control law and the ARMAX control law.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.691

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 integrity0.0000.000
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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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 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

Citations3
Published2003
Admission routes1
Has abstractyes

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