Robust stabilization and <i>H</i><sub>∞</sub> control for discrete-time stochastic genetic regulatory networks with time delays
Bibliographic record
Abstract
This paper presents some novel results on robust stabilization and H∞ control design for a class of uncertain discrete-time stochastic genetic regulatory networks (GRNs) with time-varying delays. The GRNs under consideration are subject to stochastic noise, time-varying, and norm bounded parameter uncertainties. By constructing a new Lyapunov–Krasovskii functional that contains some novel triple summation terms, we propose a state feedback gene controller to guarantee that the considered GRN is mean-square asymptotically stable about its equilibrium point for all admissible uncertainties. The other issue is to design a H∞ feedback gene controller so that the GRN is robustly stable with a prescribed H∞ disturbance attenuation level for all admissible uncertainties and for all delays to satisfy both the lower bound and upper bound of the interval time-varying delay. The obtained conditions are derived in terms of linear matrix inequalities (LMIs), which can be easily verified via the LMI toolbox. Finally, the control scheme has been implemented in a gene network model to illustrate the applicability and usefulness of the obtained results.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".