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H∞ Control and Filtering Problems for Markov Jump Systems with Time Delay

2003· book-chapter· en· W76713672 on OpenAlexaff
El‐Kébir Boukas, Zi-Kuan Liu

Bibliographic record

VenueBirkhäuser Boston eBooks · 2003
Typebook-chapter
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDisturbance (geology)Control theory (sociology)Computer scienceLTI system theoryController (irrigation)Context (archaeology)JumpInvariant (physics)Control (management)Linear systemControl engineeringEngineeringMathematicsArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

Practical systems are always subject to exogenous disturbance input which can cause performances degradations. The tools we developed are unfortunately inefficient for eliminating the effect of the disturbance. One alternative is to use the Roocontrol, which entails searching for a controller that stabilizes the system, and at the same time assures disturbance rejection at a given level. The7-l oo control problem has received considerable attention and thus numerous achievements have been reported to the literature. In the linear time-invariant context, early7-1control theory emerged as a frequency domain design technique [83].

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.172
Teacher spread0.162 · 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
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
Published2003
Admission routes1
Has abstractyes

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