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Cited by 555

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

Jinde Cao, Jun Wang·2003·IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications

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.

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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

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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
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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

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