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Record W2028918340 · doi:10.1049/iet-cta:20060413

Exact discretisation of a scalar differential Riccati equation with constant parameters

2007· article· en· W2028918340 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

VenueIET Control Theory and Applications · 2007
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsUniversity of SaskatchewanDefence Research and Development Canada
FundersJapan Society for the Promotion of Science
KeywordsDiscretizationRiccati equationMathematicsDifferential equationConstant (computer programming)Algebraic Riccati equationNonlinear systemScalar (mathematics)Exact differential equationControl theory (sociology)Mathematical analysisApplied mathematicsSampling (signal processing)Linear differential equationComputer scienceFilter (signal processing)Geometry

Abstract

fetched live from OpenAlex

An exact, first-order, discrete-time model that gives correct values at the sampling instants for any sampling interval is derived for a nonlinear system whose dynamics are governed by a scalar Riccati differential equation with constant parameters. The model is derived by transforming the given differential equation into a stable linear form to which the invariant discretisation is applied. This is in contrast with other existing methods which result in a second-order and usually unstable form and which is not suitable for on-line digital control purposes. Simulation results are presented to show that the proposed method is always exact at the sampling instants, whereas the popular forward difference model can be divergent unless the sampling interval is sufficiently small.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.407

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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