Stability analysis of delayed cellular neural networks
Neural Networks
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Global exponential stability of delayed Hopfield neural networks
Neural Networks
Data-reusing recurrent neural adaptive filters
Neural Computation
Stability of Time-Delay Systems
Stability of Time-Delay Systems
IEEE Transactions on Circuits and Systems II: Express Briefs
New globally asymptotic stability criteria for delayed cellular neural networks
IEEE Transactions on Circuits and Systems II: Express Briefs
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Robust stability of Cohen-Grossberg neural networks via state transmission matrix
IEEE Transactions on Neural Networks
New Lyapunov-Krasovskii functionals for global asymptotic stability of delayed neural networks
IEEE Transactions on Neural Networks
Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neurocomputing with time delay analysis for solving convex quadratic programming problems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Stability Analysis for Neural Networks With Time-Varying Interval Delay
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A New Criterion of Delay-Dependent Asymptotic Stability for Hopfield Neural Networks With Time Delay
IEEE Transactions on Neural Networks
Further Results on Delay-Dependent Stability Criteria of Neural Networks With Time-Varying Delays
IEEE Transactions on Neural Networks
Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays
IEEE Transactions on Neural Networks
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This paper studies the stability problem of a class of recurrent neural networks (RNNs) with multiple delays. By using an augmented matrix-vector transformation for delays and a novel line integral-type Lyapunov-Krasovskii functional, a less conservative delay-dependent global asymptotical stability criterion is first proposed for RNNs with multiple delays. The obtained stability result is easy to check and improve upon the existing ones. Then, two numerical examples are given to verify the effectiveness of the proposed criterion.