Global Stability of a General Class of Discrete-Time Recurrent Neural Networks
Neural Processing Letters
IEEE Transactions on Neural Networks
Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay
Information Sciences: an International Journal
Synchronization control of a class of memristor-based recurrent neural networks
Information Sciences: an International Journal
New Delay-Dependent Exponential Stability for Neural Networks With Time Delay
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Solving Quadratic Programming Problems by Delayed Projection Neural Network
IEEE Transactions on Neural Networks
Stability Analysis for Neural Networks With Time-Varying Interval Delay
IEEE Transactions on Neural Networks
Delay-Dependent Stability for Recurrent Neural Networks With Time-Varying Delays
IEEE Transactions on Neural Networks
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In this paper, we investigate exponential stability of delayed recurrent neural networks. By using the delay partitioning method, some sufficient conditions are established to guarantee exponential stability of delayed recurrent neural networks under two different conditions with constructing new Lyapunov-Krasvoskii functional. This partitioning approach can reduce the conservatism comparing with some previous results of stability. At last, numerical examples are given out to show the effectiveness and advantage of our results.