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Record W2513562266 · doi:10.1139/cjp-2016-0086

Effects of initial conditions and coupling competition modes on behaviors of coupled non-identical fractional-order bistable oscillators

2016· article· en· W2513562266 on OpenAlexvenueno aff
Liming Wang

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicChaos control and synchronization
Canadian institutionsnot available
Fundersnot available
KeywordsBistabilityPhysicsCoupling (piping)Oscillation (cell signaling)Synchronization (alternating current)Order (exchange)Statistical physicsAmplitudeCoupling strengthPhase (matter)Classical mechanicsControl theory (sociology)Quantum mechanicsTopology (electrical circuits)Condensed matter physics

Abstract

fetched live from OpenAlex

The effects of the initial conditions and the coupling competition modes on the dynamic behaviors of coupled non-identical fractional-order bistable oscillators are investigated intensively and the various phenomena are explored. The coupled system can be controlled to form chaos synchronization, chaos anti-phase synchronization, amplitude death, oscillation death, etc., by setting the initial conditions or selecting the coupling competition modes. Depending on whether the arbitrary initial conditions can let two coupled oscillators stop oscillating, the dynamic behaviors of the coupled system are further classified into three types, that is, both of oscillators stop oscillating, only one oscillator stops oscillating, and none of oscillators stop oscillating. Based on the principle of Monte Carlo method, the percentages of three types of dynamic behaviors are calculated for the different coupling competition modes and the dynamic behaviors of the coupled system are characterized from the perspective of statistics. Moreover, the mechanism behind the various phenomena is explained in detail by the concept of boundary layer and the optimum coupling competition modes are found.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.267

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 science0.0000.000
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 head0.006
GPT teacher head0.243
Teacher spread0.238 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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