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Record W2989443623 · doi:10.1109/tsmc.2019.2946189

Nonfragile Sampled-Data Filtering of Uncertain Fuzzy Systems With Time-Varying Delays

2019· article· en· W2989443623 on OpenAlex
J. S. Luo, Xinzhi Liu, Wenhong Tian, Shouming Zhong, Kaibo Shi

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Systems Man and Cybernetics Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsUniversity of Waterloo
FundersSichuan Province Science and Technology Support ProgramChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBounding overwatchControl theory (sociology)Fuzzy logicController (irrigation)Computer scienceFuzzy control systemStability (learning theory)MathematicsMathematical optimizationControl (management)Artificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

This article studies the nonfragile sampled-data filtering problems for Takagi-Sugeno fuzzy systems with uncertainties and delays. By introducing two delay-product-type terms and other augmented terms, a novel Lyapunov-Krasovskii functional containing more detailed information of time delays is constructed. With the help of some new bounding inequalities, improved stability results for the addressed systems are established. Besides, a resilient sampled-data controller is devised with less cost. Furthermore, the efficiency of the obtained criteria is illustrated by a numerical example.

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 categoriesMeta-epidemiology (narrow)
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 score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.209
Teacher spread0.191 · 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