Stability analysis of non-autonomous stochastic Cohen–Grossberg neural networks

Abstract

This paper is concerned with pth moment exponential stability of stochastic Cohen-Grossberg neural networks (SCGNN) with time-varying connection matrix and delays. With the help of Lyapunov function, stochastic analysis technique and the generalized Halanay inequality, a set of novel sufficient conditions on pth moment exponential stability for SCGNN is given. These results are helpful to design exponentially stable non-autonomous CohenGrossberg neural networks when stochastic effects are taken into consideration in practice.

Switched Projective Synchronization of the Stochastic Newton-Leipnik System

“…It is worth pointing out that like time delays, parameter uncertainties and stochastic factors are ubiquitous in both natural and man-made systems, for instance, sunshine duration and solar irradiation were modeled in a stochastic way [18]. There have already some results concerning stochastic factors, such as stochastic noise and stochastic delay [19][20][21]. However, the study about chaotic systems with random paramter is not many [22][23][24].…”

Global asymptotic stability of stochastic BAM neural networks with distributed delays and reaction–diffusion terms

“…In real nervous systems, synaptic transmission is a noisy process brought on by random fluctuations from the release of neurotransmitters and other probabilistic causes, as stated in [7][8][9][10][11][12][13]. Recently, the stability of discrete neural networks and discrete stochastic neural networks with probability-distribution delay and time varying delays are widely investigated by several authors [14][15][16][17][18][19][20][21].…”

Dynamic analysis of Markovian jumping impulsive stochastic Cohen–Grossberg neural networks with discrete interval and distributed time-varying delays

“…This is pointed out by [20], in real nervous system and in the implementation of artificial neural networks, synaptic transmission is a noisy process brought on by random fluctuations from the release of neurotransmitters and other probabilistic causes, hence, noise is unavoidable and should be taken into consideration in modeling. Therefore, it is of practical importance to study the stochastic effects on the stability property of delayed Cohen-Grossberg neural networks, see for example [21][22][23][24].…”