This paper formulates the model and then studies its dynamics of a system of linearly and
diffusively coupled identical delayed neural networks (DNNs), which is generalization of …

In this paper, the global exponential stability and periodicity of a class of recurrent neural
networks with time delays are addressed by using Lyapunov functional method and …

The main purpose of developing stability theory is to examine dynamic responses of a
system to disturbances as the time approaches infinity. It has been and still is the object of …

This paper derives a new sufficient condition for the exponential stability of the equilibrium
point for delayed neural networks with time varying delays by employing a Lyapunov …

This paper is concerned with the global robust stability of a class of delayed interval
recurrent neural networks which contain time-invariant uncertain parameters whose values …

This Letter presents some new sufficient conditions for the uniqueness and global
asymptotic stability (GAS) of the equilibrium point for a larger class of neural networks with …

Stochastic effects to the stability property of Hopfield neural networks (HNN) with discrete
and continuously distributed delay are considered. By using the method of variation …

This work presents a sufficient condition for the existence, uniqueness, and global robust
stability of the equilibrium point for Hopfield-type delayed neural networks. The result …

Neural networks are developed to model the behavior of the brain. One crucial question in
this field pertains to when and how a neural network can memorize a given set of patterns …

Since the outstanding and pioneering research work of Hopfield on recurrent neural
networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong …