New result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks

New Result concerning Mean Square Exponential Stability of Uncertain Stochastic Delayed Hopfield Neural Networks

By using the Lyapunov functional method, stochastic analysis, and LMI (linear matrix inequality) approach, the mean square exponential stability of an equilibrium solution of uncertain stochastic Hopfield neural networks with delayed is presented. The proposed result generalizes and improves previous work. An illustrative example is also given to demonstrate the effectiveness of the proposed re...

Mean square exponential stability of stochastic delay cellular neural networks

The dynamical behaviors of stochastic neural networks have appeared as a novel subject of research and applications, such as optimization, control, and image processing(see [1-12]). Obviously, finding stability criteria for these neural networks becomes an attractive research problem of importance. Some well results have just appeared, for example, in [1-5], for stochastic delayed Hopfield neur...

Improved Exponential Stability Analysis for Delayed Recurrent Neural Networks

This paper studies the problem of exponential stability analysis for recurrent neural networks with time-varying delay.By establishing a suitable augmented LyapunovCKrasovskii function and a novel sufficient condition is obtained to guarantee the exponential stability of the considered system.In order to get a less conservative results of the condition,zero equalities and reciprocally convex ap...

Exponential input-to-state stability of composite stochastic systems

Input output stability of recurrent neural networks

i Foreword Recurrent neural networks are an attractive tool for both practical applications and for the modeling of biological nerve nets, but their successful application requires an understanding of their dynamical properties, in particular, their stability. The present work provides an in-depth study of this challenging issue and contributes a number of new results that are also important fo...