Synchronization in complex delayed dynamical networks with impulsive effects

Zhou, Jin; Xiang, Ling; Liu, Zengrong

doi:10.1016/j.physa.2007.05.060

Cited by 181 publications

(103 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the issue of controlled synchronization in complex dynamical networks has become a rather significant topic in both theoretical research and practical applications [1][2][3]. It is known that in theory and practice, impulsive control has been widely used to stabilize and synchronize chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Exponential Synchronization of the Hopfield Neural Networks with New Chaotic Strange Attractor

Gui¹,

Wang

2017

ITM Web Conf.

View full text Add to dashboard Cite

Abstract-This paper studies the problems of exponential synchronization for the Hopfield neural networks with impulsive effects and Gui chaotic strange attractor. By employing the Lyapunov functional method of impulsive functional differential equations, some criteria for synchronization between two impulsive neural networks are derived. An illustrative example is provided to show the effectiveness and feasibility of the proposed method and results.

show abstract

Section: Introductionmentioning

confidence: 99%

Exponential Synchronization of the Hopfield Neural Networks with New Chaotic Strange Attractor

Gui¹,

Wang

2017

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…Time delays may cause bifurcation, oscillation, divergence or instability of complex networks [15][16][17]. Thus, it is extremely important to study the dynamics of complex networks considering time delays, which has become a hotly discussed issue in the area of complex networks studies [18][19][20][21][22][23]. In addition, a kind of complex networks called complex spatiotemporal networks (e.g., biological systems [24]) have also attracted researchers' attention in the past few years.…”

Section: Introductionmentioning

confidence: 99%

Projective Exponential Synchronization for a Class of Complex PDDE Networks with Multiple Time Delays

Yang

Qiu

et al. 2015

Entropy

View full text Add to dashboard Cite

This paper addresses the problem of projective exponential synchronization for a class of complex spatiotemporal networks with multiple time delays satisfying the homogeneous Neumann boundary conditions, where the network is modeled by coupled partial differential-difference equations (PDDEs). A distributed proportional-spatial derivative (P-sD) controller is designed by employing Lyapunov's direct method and Kronecker product. The controller ensures the projective exponential synchronization of the PDDE network. The main result of this paper is presented in terms of standard linear matrix inequality (LMI). A numerical example is provided to show the effectiveness of the proposed design method.

show abstract

“…They are now recognized as candidates for information processing systems and have been successfully applied to associative memory, pattern recognition, automatic control, model identification, optimization problems, etc. (we refer to reader [3][4][5][6][7][8][9][10]). Therefore, the study of stability of HNN has caught many researchers' attention.…”

Section: Introductionmentioning

confidence: 99%

Uniform Asymptotic Stability and Global Asymptotic Stability for Time-Delay Hopfield Neural Networks

Arbi

Aouiti

Touati

2012

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Abstract. In this paper, we consider the uniform asymptotic stability and global asymptotic stability of the equilibrium point for time-delays Hopfield neural networks. Some new criteria of the system are derived by using the Lyapunov functional method and the linear matrix inequality approach for estimating the upper bound of the derivative of Lyapunov functional. Finally, we illustrate a numerical example showing the effectiveness of our theoretical results.

show abstract

Synchronization in complex delayed dynamical networks with impulsive effects

Cited by 181 publications

References 23 publications

Exponential Synchronization of the Hopfield Neural Networks with New Chaotic Strange Attractor

Exponential Synchronization of the Hopfield Neural Networks with New Chaotic Strange Attractor

Projective Exponential Synchronization for a Class of Complex PDDE Networks with Multiple Time Delays

Uniform Asymptotic Stability and Global Asymptotic Stability for Time-Delay Hopfield Neural Networks

Contact Info

Product

Resources

About