Stability problems of continuous-time recurrent neural networks have been extensively
studied, and many papers have been published in the literature. The purpose of this paper is …

Cellular Nonlinear/neural Network (CNN) technology is both a revolutionary concept and an
experimentally proven new computing paradigm. Analogic cellular computers based on …

The paper introduces a general class of neural networks where the neuron activations are
modeled by discontinuous functions. The neural networks have an additive interconnecting …

The paper considers a class of additive neural networks where the neuron activations are
modeled by discontinuous functions or by continuous non-Lipschitz functions. Some tools …

In this paper a novel approach to assess the stability of dynamic neural networks is
presented. Using a Lyapunov function, we determine conditions to guarantee input-to-state …

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 …

Sufficient conditions are obtained for the existence and asymptotic stability of a unique
equilibrium of a Hopfield-type neural network with Lipschitzian activation functions without …

This paper investigates the absolute exponential stability of a general class of delayed
neural networks, which require the activation functions to be partially Lipschitz continuous …

In this paper, we use the matrix measure technique to study the stability of dynamical neural
networks. Testable conditions for global exponential stability of nonlinear dynamical systems …

A sufficient condition related to the existence of a unique equilibrium point and its global
asymptotic stability for cellular network networks with delay (DCNNs) is derived. It is shown …