An Event-Triggering Approach to Recursive Filtering for Complex Networks With State Saturations and Random Coupling Strengths

2020 ◽  
Vol 31 (10) ◽  
pp. 4279-4289 ◽  
Author(s):  
Hongyu Gao ◽  
Hongli Dong ◽  
Zidong Wang ◽  
Fei Han
Keyword(s):  
Complex Networks ◽  
Recursive Filtering ◽  
Event Triggering ◽  
Coupling Strengths ◽  
Random Coupling

scholarly journals Synchronization of Complex Networks with Random Coupling Strengths and Mixed Probabilistic Time-Varying Coupling Delays Using Sampled Data

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jian-An Wang
Keyword(s):  
Complex Networks ◽  
Distributed Delay ◽  
Sampling Period ◽  
Linear Matrix ◽  
Mixed Delays ◽  
Time Varying ◽  
Sampled Data ◽  
Coupling Strengths ◽  
Error Dynamics ◽  
Random Coupling

The sampled-data synchronization problem for complex networks with random coupling strengths, probabilistic time-varying coupling delay, and distributed delay (mixed delays) is investigated. The sampling period is assumed to be time varying and bounded. By using the properties of random variables and input delay approach, new synchronization error dynamics are constructed. Based on the delay decomposition method and reciprocally convex approach, a delay-dependent mean square synchronization condition is established in terms of linear matrix inequalities (LMIs). According to the proposed condition, an explicit expression for a set of desired sampled-data controllers can be achieved by solving LMIs. Numerical examples are given to demonstrate the effectiveness of the theoretical results.

scholarly journals Synchronization of Complex Networks with Time-Varying Delayed Dynamical Nodes via Pinning Control

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Shuguo Wang ◽  
Hongxing Yao ◽  
Qiuxiang Bian
Keyword(s):  
Numerical Simulation ◽  
Complex Networks ◽  
Pinning Control ◽  
Effective Control ◽  
Time Varying ◽  
Time Varying Delay ◽  
Control Scheme ◽  
Adaptive Coupling ◽  
Coupling Strengths ◽  
Varying Delay

This paper investigates the pinning synchronization of nonlinearly coupled complex networks with time-varying coupling delay and time-varying delay in dynamical nodes. Some simple and useful criteria are derived by constructing an effective control scheme and adjusting automatically the adaptive coupling strengths. To validate the proposed method, numerical simulation examples are provided to verify the correctness and effectiveness of the proposed scheme.

scholarly journals Sampled-Data Synchronization for Complex Dynamical Networks with Time-Varying Coupling Delay and Random Coupling Strengths

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Jian-An Wang ◽  
Xin-Yu Wen
Keyword(s):  
Sampling Period ◽  
Complex Dynamical Networks ◽  
Data Synchronization ◽  
Linear Matrix ◽  
Time Varying ◽  
Sampled Data ◽  
Dynamical Networks ◽  
Coupling Delay ◽  
Coupling Strengths ◽  
Random Coupling

This paper is concerned with the problem of sampled-data synchronization for complex dynamical networks (CDNs) with time-varying coupling delay and random coupling strengths. The random coupling strengths are described by normal distribution. The sampling period considered here is assumed to be less than a given bound. By taking the characteristic of sampled-data system into account, a discontinuous Lyapunov functional is constructed, and a delay-dependent mean square synchronization criterion is derived. Based on the proposed condition, a set of desired sampled-data controllers are designed in terms of linear matrix inequalities (LMIs) that can be solved effectively by using MATLAB LMI Toolbox. Numerical examples are given to demonstrate the effectiveness of the proposed scheme.

ON THE LOCAL STOCHASTIC STABILITY OF NONLINEAR COMPLEX NETWORKS

2010 ◽  
Vol 20 (01) ◽  
pp. 177-184 ◽  
Author(s):  
ZHI-LONG HUANG ◽  
ZHOU YAN ◽  
XIAO-LING JIN ◽  
GUANRONG CHEN
Keyword(s):  
Lyapunov Exponent ◽  
Theoretical Analysis ◽  
Complex Networks ◽  
Stochastic Stability ◽  
Largest Lyapunov Exponent ◽  
Stochastic Perturbations ◽  
Approximate Analytical Solutions ◽  
Coupling Strengths ◽  
The Largest Lyapunov Exponent ◽  
The One

The local stochastic stability of nonlinear complex networks is studied, subject to stochastic perturbations to the coupling strengths and stochastic parametric excitations to the nodes. The complex network is first linearized at its trivial solution and then the linearized network is reduced to N independent subsystems by using a suitable linear transformation, where N is the size of the network. The largest Lyapunov exponent for each subsystem is then calculated and all the approximate analytical solutions are evaluated for some specific cases. It is found that the largest Lyapunov exponent among all subsystems is the one associated with the subsystem that has the largest or the smallest eigenvalue of the configuration matrix of the network. Finally, an example is given to demonstrate the validity and accuracy of the theoretical analysis.

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