Dynamical Behaviors of a Large Class of General Delayed Neural Networks
Neural Computation
Robust stability for interval Hopfield neural networks with time delay
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
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International Journal of Computer Mathematics - COMPLEX NETWORKS
Expert Systems with Applications: An International Journal
Further Stability Analysis for Neural Networks with Time-Varying Interval Delay
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Robust stabilization of linear differential inclusion system with time delay
Mathematics and Computers in Simulation
IEEE Transactions on Neural Networks
Globe robust stability analysis for interval neutral systems
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Original Articles: Noise suppress exponential growth for hybrid Hopfield neural networks
Mathematics and Computers in Simulation
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In this paper, the global robust stability is investigated for interval neural networks with multiple time-varying delays. The neural network contains time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. Without assuming both the boundedness on the activation functions and the differentiability on the time-varying delays, a new sufficient condition is presented to ensure the existence, uniqueness, and global robust stability of equilibria for interval neural networks with multiple time-varying delays based on the Lyapunov-Razumikhin technique as well as matrix inequality analysis. Several previous results are improved and generalized, and an example is given to show the effectiveness of the obtained results.