The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Synchronization Control for Nonlinear Stochastic Dynamical Networks: Pinning Impulsive Strategy
Abstract
In this paper, a new control strategy is proposed for the synchronization of stochastic dynamical networks with nonlinear coupling. Pinning state feedback controllers have been proved to be effective for synchronization control of state-coupled dynamical networks. We will show that pinning impulsive controllers are also effective for synchronization control of the above mentioned dynamical networks. Some generic mean square stability criteria are derived in terms of algebraic conditions, which guarantee that the whole state-coupled dynamical network can be forced to some desired trajectory by placing impulsive controllers on a small fraction of nodes. An effective method is given to select the nodes which should be controlled at each impulsive constants. The proportion of the controlled nodes guaranteeing the stability is explicitly obtained, and the synchronization region is also derived and clearly plotted. Numerical simulations are exploited to demonstrate the effectiveness of the pinning impulsive strategy proposed in this paper.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Cited by
He, W., Qian, F., Lam, J., Chen, G., Han, Q.-L., & Kurths, J. (2015) · Automatica · doi:10.1016/j.automatica.2015.09.028 · Cited by 429
Self-citation
Metadata
- Authors
- Lu, J., Kurths, J., Cao, J., Mahdavi, N., & Huang, C.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2012-02
- Volume
- 23
- Issue
- 2
- Pages
- 285-292
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2063246107
- Cited by
- 418
- References
- 1
- FWCI
- 57.82
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Synchronization Control for Nonlinear Stochastic Dynamical Networks: Pinning Impulsive Strategy. https://app.cochat.ai/papers/10.1109/tnnls.2011.2179312
- The paper
- Lu, J., Kurths, J., Cao, J., Mahdavi, N., & Huang, C. (2012). Synchronization Control for Nonlinear Stochastic Dynamical Networks: Pinning Impulsive Strategy. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2011.2179312
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
