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Record W1558676959 · doi:10.1109/iscas.2006.1693808

Realization of MIMO linear discrete-time systems with minimum L/sub 2/-sensitivity and no overflow oscillations

2006· article· en· W1558676959 on OpenAlexaff
Takao Hinamoto, Osemekhian I. Omoifo, Wu-Sheng Lu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRealization (probability)MIMOSensitivity (control systems)MinificationControl theory (sociology)Mathematical optimizationLinear systemMeasure (data warehouse)ScalingComputer scienceRange (aeronautics)Discrete time and continuous timeMatrix (chemical analysis)MathematicsEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

The minimization problem of an L/sub 2/-sensitivity measure subject to L/sub 2/-scaling constraints on the dynamic range for multi-input/multi-output (MIMO) linear discrete-time systems is formulated. An iterative technique is developed to solve the constrained optimization problem directly. The proposed solution method largely relies on the use of a Lagrange function and some matrix-theoretic techniques. A numerical example is presented to illustrate the utility of the proposed technique.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations0
Published2006
Admission routes1
Has abstractyes

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