M-matrices and global convergence of discontinuous neural networks: Research Articles
International Journal of Circuit Theory and Applications
Robust Stability Criterion for Delayed Neural Networks with Discontinuous Activation Functions
Neural Processing Letters
A Winner-Take-All Neural Networks of N Linear Threshold Neurons without Self-Excitatory Connections
Neural Processing Letters
Almost sure exponential stability of recurrent neural networks with Markovian switching
IEEE Transactions on Neural Networks
A delayed projection neural network for solving linear variational inequalities
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Robust state estimation for neural networks with discontinuous activations
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
Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays
IEEE Transactions on Neural Networks
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In this paper, we integrate a class of delayed neural networks with discontinuous activations, which are not supposed to be bounded or nondecreasing. Conditions of existence of an equilibrium point are established by means of the Leray-Schauder theorem of set-valued maps. Then, the existence of solutions is proved based on viability theorem. Furthermore, global asymptotical stability of the networks is studied by using Lyapunov-Krasovskii stability theory. The results of global asymptotical stability are in term of linear matrix inequality. The obtained results extend previous works on global stability of delayed neural networks with discontinues activations.