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Record W1985357728 · doi:10.1109/cdc.2011.6161313

Stochastic stability of semi-Markov jump linear systems: An LMI approach

2011· article· en· W1985357728 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMathematicsMarkov processMarkov chainApplied mathematicsLinear systemMarkov modelWeibull distributionMarkov renewal processLyapunov functionMarkov propertyControl theory (sociology)Mathematical optimizationComputer scienceNonlinear systemMathematical analysisStatistics

Abstract

fetched live from OpenAlex

The semi-Markov jump linear system is more general than the classic Markov jump linear system. In the semi-Markov jump linear systems, the governing stochastic process is not a Markov process, but a semi-Markov process. Instead of the exponential distribution for the sojourn-time in each mode in the jump linear system, the Weibull distribution is considered in this paper. By deriving the infinitesimal generator for the Lyapunov function of the semi-Markov jump linear system, the numerically testable sufficient conditions for stochastic stability of semi-Markov jump linear systems are obtained. Numerical examples are provided to validate the proposed sufficient stochastic stability conditions.

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)0.001
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: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.708

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 head0.039
GPT teacher head0.212
Teacher spread0.172 · 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

Quick stats

Citations105
Published2011
Admission routes1
Has abstractyes

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