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Record W2129558416 · doi:10.1109/ssst.2010.5442796

Robust multiple model adaptive control using fuzzy fusion

2010· article· en· W2129558416 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
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceFuzzy logicControl theory (sociology)Benchmark (surveying)Fuzzy control systemController (irrigation)Robust controlAdaptive controlAdaptive neuro fuzzy inference systemNeuro-fuzzyControl engineeringControl systemControl (management)Artificial intelligenceEngineering

Abstract

fetched live from OpenAlex

A robust multiple model adaptive control strategy using fuzzy fusion (RMMAC-FF) is presented in this paper. The main idea in multi-model controllers is to identify the best model of the system at any instant of time and apply the appropriate control input to it. RMMAC-FF, integrates a fuzzy robust controller, with the fuzzy multiple model adaptive estimation and a fuzzy switching to come up with a new strong methodology to control complex systems. Simulation results of the RMMAC-FF on a two-cart system, used as a benchmark problem, verify the theory and confirm the effectiveness of the proposed controller.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.042
GPT teacher head0.219
Teacher spread0.177 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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