The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Global asymptotic stability of a general class of recurrent neural networks with time-varying delays
Abstract
In this paper, the existence and uniqueness of the equilibrium point and its global asymptotic stability are discussed for a general class of recurrent neural networks with time-varying delays and Lipschitz continuous activation functions. The neural network model considered includes the delayed Hopfield neural networks, bidirectional associative memory networks, and delayed cellular neural networks as its special cases. Several new sufficient conditions for ascertaining the existence, uniqueness, and global asymptotic stability of the equilibrium point of such recurrent neural networks are obtained by using the theory of topological degree and properties of nonsingular M-matrix, and constructing suitable Lyapunov functionals. The new criteria do not require the activation functions to be differentiable, bounded or monotone nondecreasing and the connection weight matrices to be symmetric. Some stability results from previous works are extended and improved. Two illustrative examples are given to demonstrate the effectiveness of the obtained 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
Cites
Linear Matrix Inequalities in System and Control Theory
Boyd, S., El Ghaoui, L., Feron, E., & Balakrishnan, V. (1994) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970777 · Cited by 21,687
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Theory of Functional Differential Equations
Hale, J. K. (1977) · Applied Mathematical Sciences · doi:10.1007/978-1-4612-9892-2 · Cited by 6,870
Nonnegative Matrices in the Mathematical Sciences
(1979) · Elsevier eBooks · doi:10.1016/c2013-0-10361-3 · Cited by 5,451
Bidirectional associative memories
Kosko, B. (1988) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/21.87054 · Cited by 2,070
Stability of analog neural networks with delay
Marcus, C. M., & Westervelt, R. M. (1989) · Physical Review A · doi:10.1103/physreva.39.347 · Cited by 971
Robust Control and Filtering for Time-Delay Systems
Mahmoud, M. S. (2018) · CRC Press eBooks · doi:10.1201/9781315214948 · Cited by 683
Delay-independent stability in bidirectional associative memory networks
Gopalsamy, K., & He, X.-Z. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.329700 · Cited by 560
Global Attractivity in Delayed Hopfield Neural Network Models
van den Driessche, P., & Zou, X. (1998) · SIAM Journal on Applied Mathematics · doi:10.1137/s0036139997321219 · Cited by 428
Robust stability for interval Hopfield neural networks with time delay
Liao, X., & Yu, J. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.712187 · Cited by 265
Cited by
Kumar, R., Srivastava, S., & Gupta, J. R. P. (2017) · ISA Transactions · doi:10.1016/j.isatra.2017.01.022 · Cited by 102
Metadata
- Authors
- Cao, J., & Wang, J.
- Venue
- IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
- Published
- 2003-01
- Volume
- 50
- Issue
- 1
- Pages
- 34-44
- ISSN
- 1057-7122 (ISSN-L)
- OpenAlex
- W2086635487
- Cited by
- 555
- References
- 21
- FWCI
- 65.90
- 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: Global asymptotic stability of a general class of recurrent neural networks with time-varying delays. https://app.cochat.ai/papers/10.1109/tcsi.2002.807494
- The paper
- Cao, J., & Wang, J. (2003). Global asymptotic stability of a general class of recurrent neural networks with time-varying delays. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications. https://doi.org/10.1109/tcsi.2002.807494
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.