Global exponential stability of discrete-time Hopfield neural network models with unbounded delays

On Global Exponential Stability of Discrete-Time Hopfield Neural Networks with Variable Delays

Global exponential stability of a class of discrete-time Hopfield neural networks with variable delays is considered. By making use of a difference inequality, a new global exponential stability result is provided. The result only requires the delay to be bounded. For this reason, the result is milder than those presented in the earlier references. Furthermore, two examples are given to show th...

Global Exponential Stability of Hopfield-type Neural Networks with Time Delays

The paper is concerned with the improvement of sufficient conditions for the exponential stability of Hopfield-type neural networks displaying interaction delays. The results are based on a method obtained in our previous work that combines an idea suggested by Malkin for studying the absolute stability of a nonlinear system via their linearisations and a procedure proposed by Kharitonov for co...

Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays

Exponential stability of implicit Euler, discrete-time Hopfield neural networks

The exponential stability of continuous-time Hopfield neural networks is not preserved when implemented on digital computers by means of explicit numerical methods, whereas the implicit (or backward) Euler method preserves this exponential stability under exactly the same sufficient conditions as those previously obtained for the continuous model. The proof is based on the nonlinear measure app...

Exponential Stability of Discrete-Time Hopfield Neural Networks

In this paper, some sufficient conditions for the local and global exponential stability of the discrete-time Hopfield neural networks with general activation functions are derived, which generalize those existing results. By means of M-matrix theory and some inequality analysis techniques, the exponential convergence rate of the neural networks to the equilibrium is estimated, and for the loca...