New robust passivity criteria for discrete-time genetic regulatory networks with Markovian jumping parameters
Bibliographic record
Abstract
In this paper, we present some novel results for robust passivity of a class of uncertain discrete-time Markovian jumping genetic regulatory networks (GRNs) with time-varying delays. By constructing a new set of Lyapunov–Krasovskii functionals, together with the matrix inequality technique, we provide a new set of results for the passiveness, and also ensure the mean-square asymptotical stability (internally) of the considered GRNs about their equilibrium points for all time delays satisfying both the lower bound and upper bound of the interval time-varying delay. Further, the obtained results are extended to deal with the robust passiveness of the considered GRNs for all admissible uncertainties. The obtained conditions are derived in terms of linear matrix inequalities (LMIs), which can be easily verified via the LMI toolbox. Finally, numerical examples with simulation results are provided for the GRN model to illustrate the applicability and usefulness of the theory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".