Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
Stability analysis of delayed cellular neural networks
Neural Networks
Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
Globally exponential stability conditions for cellular neural networks with time-varying delays
Applied Mathematics and Computation
Global asymptotic stability of delayed bi-directional associative memory neural networks
Applied Mathematics and Computation
Global stability for cellular neural networks with time delay
IEEE Transactions on Neural Networks
An analysis of global asymptotic stability of delayed cellular neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Inferring gene regulatory networks from temporal expression profiles under time-delay and noise
Computational Biology and Chemistry
New results concerning the exponential stability of delayed neural networks with impulses
Computers & Mathematics with Applications
Variable-time impulses in BAM neural networks with delays
Neurocomputing
| Hi-index | 0.98 |
In this paper, the existence and uniqueness of the equilibrium point and stability of the cellular neural networks (CNNs) with time-varying delays are analyzed and proved. Several global exponential stability conditions of the neural networks are obtained by the delay differential inequality and matrix measures approach. The obtained results are extensions of the earlier literature. The approach used in this paper is also suitable for delayed Hopfield neural networks and delayed bi-directional associative memory neural networks whose activation functions are often nondifferentiable or unbounded. Two simulation examples in comparison to previous results in literature are shown to check the theory in this paper.