This work addresses the stability study for stochastic cellular neural networks with time-varying delays. By utilizing the new research technique of the fixed point theory, we find some new and concise sufficient conditions ensuring the existence and uniqueness as well as mean-square global exponential stability of the solution. The presented algebraic stability criteria are easily checked and do not require the differentiability of delays. The paper is finally ended with an example to show the effectiveness of the obtained results
We firstly employ the fixed point theory to study the stability of cellular neural networks without ...
This paper investigates the problem of exponential stability for a class of uncertain discrete-time ...
In this paper, the global exponential stability is investigated for the discrete-time uncertain stoc...
This paper is concerned with the problem of mean-square exponential stability of uncertain neural ne...
AbstractThe stability of a class of stochastic Recurrent Neural Networks with time-varying delays is...
This brief investigates the problem of mean square exponential stability of uncertain stochastic del...
Exponential stability in mean square of stochastic delay recurrent neural networks is investigated i...
We present new conditions for asymptotic stability and exponential stability of a class of stochasti...
This Letter concerns with the mean square exponential stability of uncertain stochastic delayed neur...
The authors in their papers (Liao and Mao, Stochast. Anal. Appl. 14 (2) (1996a) 165-185; Neural, Par...
This paper is concerned with analyzing mean square exponential stability of stochastic delayed neura...
This paper deals with the global exponential stability analysis problem for a general class of uncer...
In this paper, the global exponential stability problem is studied for a class of discrete-time unce...
This paper investigates the problem of mean-square asymptotic stability of uncertain neural networks...
In this Letter, a thorough analysis of existence, uniqueness and globally exponential stability of t...
We firstly employ the fixed point theory to study the stability of cellular neural networks without ...
This paper investigates the problem of exponential stability for a class of uncertain discrete-time ...
In this paper, the global exponential stability is investigated for the discrete-time uncertain stoc...
This paper is concerned with the problem of mean-square exponential stability of uncertain neural ne...
AbstractThe stability of a class of stochastic Recurrent Neural Networks with time-varying delays is...
This brief investigates the problem of mean square exponential stability of uncertain stochastic del...
Exponential stability in mean square of stochastic delay recurrent neural networks is investigated i...
We present new conditions for asymptotic stability and exponential stability of a class of stochasti...
This Letter concerns with the mean square exponential stability of uncertain stochastic delayed neur...
The authors in their papers (Liao and Mao, Stochast. Anal. Appl. 14 (2) (1996a) 165-185; Neural, Par...
This paper is concerned with analyzing mean square exponential stability of stochastic delayed neura...
This paper deals with the global exponential stability analysis problem for a general class of uncer...
In this paper, the global exponential stability problem is studied for a class of discrete-time unce...
This paper investigates the problem of mean-square asymptotic stability of uncertain neural networks...
In this Letter, a thorough analysis of existence, uniqueness and globally exponential stability of t...
We firstly employ the fixed point theory to study the stability of cellular neural networks without ...
This paper investigates the problem of exponential stability for a class of uncertain discrete-time ...
In this paper, the global exponential stability is investigated for the discrete-time uncertain stoc...