Brief paper: A unified synchronization criterion for impulsive dynamical networks

Authors:
Jianquan Lu;Daniel W. C. Ho;Jinde Cao
Affiliations:
Department of Mathematics, Southeast University, Nanjing 210096, China and Department of Mathematics, City University of Hong Kong, Hong Kong, China;Department of Mathematics, City University of Hong Kong, Hong Kong, China;Department of Mathematics, Southeast University, Nanjing 210096, China
Venue:
Automatica (Journal of IFAC)
Year:
2010

Citing 13
Cited 8

Autowave principles for parallel image processing

Selcted papers from a meeting on Waves and pattern in chemical and biological media
Impulsive Systems and Control: Theory and Applications

Impulsive Systems and Control: Theory and Applications
Brief paper: Reliable dissipative control for stochastic impulsive systems

Automatica (Journal of IFAC)
Brief paper: On pinning synchronization of complex dynamical networks

Automatica (Journal of IFAC)
Impulsively control complex networks with different dynamical nodes to its trivial equilibrium

Computers & Mathematics with Applications
Brief paper: Input-to-state stability and integral input-to-state stability of nonlinear impulsive systems with delays

Automatica (Journal of IFAC)
Brief paper: Passivity-based control and synchronization of general complex dynamical networks

Automatica (Journal of IFAC)
Global exponential stability of impulsive neural networks with variable delay: an LMI approach

IEEE Transactions on Circuits and Systems Part I: Regular Papers
Brief paper: Synchronization of complex dynamical networks under recoverable attacks

Automatica (Journal of IFAC)
Pinning stabilization of linearly coupled stochastic neural networks via minimum number of controllers

IEEE Transactions on Neural Networks
Globally exponential synchronization and synchronizability for general dynamical networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pattern recognition via synchronization in phase-locked loop neural networks

IEEE Transactions on Neural Networks

Exponential stability of impulsive discrete systems with time delay and applications in stochastic neural networks: A Razumikhin approach

Neurocomputing
Finite-time filtering for discrete-time linear impulsive systems

Signal Processing
Letters: Cluster synchronization for directed complex dynamical networks via pinning control

Neurocomputing
Neural networks letter: Stability of delayed neural networks with time-varying impulses

Neural Networks
A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Exponential synchronization of impulsive complex networks with output coupling

International Journal of Automation and Computing
Second-order consensus of multi-agent systems with nonlinear dynamics via impulsive control

Neurocomputing
Synchronization of TS fuzzy complex dynamical networks with time-varying impulsive delays and stochastic effects

Fuzzy Sets and Systems

Quantified Score

Hi-index	22.14

Visualization

Abstract

This paper focuses on the problem of globally exponential synchronization of impulsive dynamical networks. Two types of impulses are considered: synchronizing impulses and desynchronizing impulses. In previous literature, all of the results are devoted to investigating these two kinds of impulses separately. Thus a natural question arises: Is there any unified synchronization criterion which is simultaneously effective for synchronizing impulses and desynchronizing impulses? In this paper, a unified synchronization criterion is derived for directed impulsive dynamical networks by proposing a concept named ''average impulsive interval''. The derived criterion is theoretically and numerically proved to be less conservative than existing results. Numerical examples including scale-free and small-world structures are given to show that our results are applicable to large-scale networks.