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Stability Analysis of Nonlinear Systems via Estimating Radial-Basis-Function-Network-Based Lyapunov Exponents From a Scalar Time Series

2012· article· en· W1968175677 on OpenAlex

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affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLyapunov exponentJacobian matrix and determinantAttractorNonlinear systemControl theory (sociology)Lyapunov functionScalar (mathematics)Inverted pendulumMathematicsApplied mathematicsSeries (stratigraphy)Robustness (evolution)Lyapunov equationComputer scienceMathematical analysisArtificial intelligencePhysics

Abstract

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The concept of Lyapunov exponents is a powerful tool for analyzing the stability of nonlinear dynamic systems especially when the mathematical models of the systems are available. However, for real world systems, such models are often unknown, and estimating these exponents reliably from experimental data is notoriously difficult. A novel method of estimating Lyapunov exponents from a time series is presented in this paper. The method combines the ideas of reconstructing the attractor of the system under study and approximating the embedded attractor through tuning a Radial-Basis-Function (RBF) network, which facilitates the derivation of the Jacobian matrices for applying the model-based algorithm. Simplified as a two-link inverted pendulum with one additional rigid foot-link, a standing biped with a Linear Quadrtic Regulator (LQR) is selected as a case study. The biped balance system has a spectrum including four negative Lyapunov exponents, of which the high numerical accuracy derived through the newly proposed method can be guaranteed even in presence of the measurement noise. We believe that the work can contribute to the stability analysis of nonlinear systems of which the dynamics are either unknown or difficult to model due to complexities.© 2012 ASME

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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 score0.431
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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

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Citations0
Published2012
Admission routes1
Has abstractyes

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