ArticlePloS one2025
Double reinforcement learning for cluster synchronization of Boolean control networks under denial of service attacks.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper investigates the asymptotic cluster synchronization of Boolean control networks (BCNs) under denial-of-service (DoS) attacks, where each state node in the network experiences random data loss following a Bernoulli distribution. First, the algebraic representation of BCNs under DoS attacks is established using the semi-tensor product (STP) of matrices. Using matrix-based methods, some necessary and sufficient algebraic conditions for BCNs to achieve asymptotic cluster synchronization under DoS attacks are derived. For both model-based and model-free cases, appropriate state feedback controllers guaranteeing asymptotic cluster synchronization of BCNs are obtained through set-iteration and double-deep Q-network (DDQN) methods, respectively. Besides, a double reinforcement learning algorithm is designed to identify suitable state feedback controllers. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.