The DOI matches its Crossref record.
Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays
Abstract
This paper is concerned with the problems of exponential stability analysis and synchronization of discrete-time switched delayed neural networks. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with time-delays. Benefitting from the delay partitioning method and the free-weighting matrix technique, the conservatism of the obtained results is reduced. In addition, the decay estimates are explicitly given and the synchronization problem is solved. The results reported in this paper not only depend upon the delay, but also depend upon the partitioning, which aims at reducing the conservatism. Numerical examples are presented to demonstrate the usefulness of the derived theoretical results.
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
Cited by
Cui, R., Chen, L., Yang, C., & Chen, M. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2694410 · Cited by 566
Synchronization in complex networks and its application – A survey of recent advances and challenges
Tang, Y., Qian, F., Gao, H., & Kurths, J. (2014) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2014.09.003 · Cited by 358
Wen, S., Zeng, Z., Huang, T., & Zhang, Y. (2014) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2294855 · Cited by 289
Circuit design and exponential stabilization of memristive neural networks
Wen, S., Huang, T., Zeng, Z., Chen, Y., & Li, P. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.011 · Cited by 178
Metadata
- Authors
- Wu, L., Feng, Z., & Lam, J.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2013-12
- Volume
- 24
- Issue
- 12
- Pages
- 1957-1972
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W1972510095
- Cited by
- 197
- References
- 0
- FWCI
- 29.08
- 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: Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays. https://app.cochat.ai/papers/10.1109/tnnls.2013.2271046
- The paper
- Wu, L., Feng, Z., & Lam, J. (2013). Stability and Synchronization of Discrete-Time Neural Networks With Switching Parameters and Time-Varying Delays. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2013.2271046
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.
