Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Shixian Luo"'
Publikováno v:
Neurocomputing. 286:41-50
The issue of exponential synchronization of stochastic time-delayed memristor-based neural networks via distributed impulsive control is considered. Based on the characteristics of memristor, the corresponding drive and response stochastic memristor-
Autor:
Shixian Luo, Yan Jiang
Publikováno v:
Circuits, Systems, and Signal Processing. 36:1426-1444
In this paper, we focus on the synchronization problem of delayed stochastic neural networks via periodically intermittent control. Two cases of time-varying bounded delay are considered: one is that the time-varying delay without any constraints on
Publikováno v:
2016 12th World Congress on Intelligent Control and Automation (WCICA).
This paper presents a new impulsive synchronization criterion of two identical reaction-diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov function
Publikováno v:
IEEE transactions on neural networks and learning systems. 27(12)
This paper presents a new impulsive synchronization criterion of two identical reaction–diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov functi
Publikováno v:
2015 34th Chinese Control Conference (CCC).
In this paper, we consider the stabilization problem of stochastic delayed neural networks (SDNNs) via periodically intermittent control. Based on a time-varying piecewise Lyapunov functional approach, a mean-square exponential stability criterion fo
Publikováno v:
2015 34th Chinese Control Conference (CCC).
In this paper, the stabilization problem of a class of diffusion neural networks via periodically intermittent sampleddata control is studied. It is assumed that the measurement of the states are taken in a finite number of fixed sampling points in t
Publikováno v:
Proceedings of the 33rd Chinese Control Conference.
In this paper, the problem of multistability analysis for a class of stochastic Hopfield neural networks is considered. By utilizing the properties of activation functions and applying Schauder's fixed-point theorem, a sufficient condition for the ex