Discrete-time analogues of integrodifferential equations modelling bidirectional neural networks
Journal of Computational and Applied Mathematics
Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
Stability and bifurcation analysis on a discrete-time neural network
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Mathematics and Computers in Simulation
Stability analysis for discrete-time Markovian jump neural networks with mixed time-delays
Expert Systems with Applications: An International Journal
Numerical simulation of periodic solutions for a class of numerical discretization neural networks
Mathematical and Computer Modelling: An International Journal
Computers & Mathematics with Applications
International Journal of Innovative Computing and Applications
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This paper is concerned with the stability analysis problem for a new class of discrete-time recurrent neural networks with mixed time-delays. The mixed time-delays that consist of both the discrete and distributed time-delays are addressed, for the first time, when analyzing the asymptotic stability for discrete-time neural networks. The activation functions are not required to be differentiable or strictly monotonic. The existence of the equilibrium point is first proved under mild conditions. By constructing a new Lyapnuov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the discrete-time neural networks to be globally asymptotically stable. As an extension, we further consider the stability analysis problem for the same class of neural networks but with state-dependent stochastic disturbances. All the conditions obtained are expressed in terms of LMIs whose feasibility can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition.