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 …

In this paper, the existence and uniqueness of the equilibrium point and its global asymptotic
stability are discussed for a general class of recurrent neural networks with time-varying …

In this paper, two related problems, global asymptotic stability (GAS) and global robust
stability (GRS) of neural networks with time delays, are studied. First, GAS of delayed neural …

In this paper, the global exponential stability and periodicity for a class of reaction–diffusion
delayed recurrent neural networks with Dirichlet boundary conditions are addressed by …

For neural networks with constant or time-varying delays, the problems of determining the
exponential stability and estimating the exponential convergence rate are studied in this …

Robust stability properties of continuous-time dynamical neural networks involving time
delay parameters have been extensively studied, and many sufficient criteria for robust …

In this paper, the author derives several sufficient conditions for the asymptotic stability of
delayed neural networks. These conditions ensure the asymptotic stability of the equilibrium …

" Illuminates the most important results of the Lyapunov and Lagrange stability theory for a
general class of dynamical systems by developing topics in a metric space independantly of …