Asymptotic Feedback Stabilization of Boolean Control Networks With Random Impulsive Disturbances
Bibliographic record
Abstract
Based on the hybrid-index model, this article investigates the asymptotic feedback set stabilization of Boolean control networks (BCNs) with random impulsive disturbances. In this model, it is assumed that the sequence of intervals between adjacent impulsive instants is independent and identically distributed. This assumption ensures that the subsequence of solutions sampled at impulsive moments is a Markov chain. Based on this assumption and the semi-tensor product (STP), random impulsive BCNs (RI-BCNs) can be converted into impulsive-interval driven probabilistic BCNs (ID-PBCNs), and the input-state transition probability matrix (IS-TPM) is constructed, the calculations of convergent target set in the hybrid domain and the time domain are discussed, and the necessary and sufficient conditions for asymptotic feedback set stabilizability are obtained. On this basis, we propose a design algorithm of state feedback controllers to stabilize RI-BCNs asymptotically with respect to a target set by using state-space partition, which enables the system to converge to a given set with the least number of impulsive intervals. Finally, the effectiveness of the obtained results is verified by simulations.
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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".