Topological Identification of Complex Dynamical Network with Communities and Node Delay

2013 ◽  
Vol 411-414 ◽  
pp. 2093-2097
Author(s):  
Jiang Ang Zhang ◽  
Yan Dong Chu ◽  
Wen Ju Du
Keyword(s):  
Parameter Identification ◽  
Hierarchical Structure ◽  
Invariance Principle ◽  
Topological Structure ◽  
Complex Dynamical Networks ◽  
Complex Dynamical Network ◽  
Network Systems ◽  
Dynamical Network ◽  
General Complex ◽  
Real Complex

Recently, various papers investigated the topology identification and parameter identification of uncertain general complex dynamical networks. However, in many real complex dynamical network systems, there exists community or hierarchical structure and node delay. Based on LaSalle’s invariance principle, in this letter, an adaptive controlling method is proposed to identify unknown topological structure for general weighted complex dynamical network with community and node delay. Illustrative simulations are provided to verify the correctness and effectiveness of the proposed scheme.

A novel method of topology identification for general complex dynamical networks with incomplete measurements

2018 ◽  
Vol 29 (05) ◽  
pp. 1840001
Author(s):  
Xinwei Wang ◽  
Guo-Ping Jiang ◽  
Xu Wu
Keyword(s):  
Linear Matrix Inequalities ◽  
Topological Structure ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Complex Dynamical Network ◽  
Topology Identification ◽  
Dynamical Network ◽  
General Complex ◽  
Novel Method ◽  
Mathematical Derivation

Recovering the topological structure of a general complex dynamical network with the incomplete measurements of transmitted drive states is investigated in this paper. The incomplete measurements, which cannot be ignored, have not been well considered in topology identification issue. Different from previous studies, we propose a novel method which can handle the situation of incomplete measurements well. The proposed method can fix the excessive deviation of the controller caused by the incomplete measurements, and overcome the special restrictions on the node dynamics raised by the previous methods. By means of LaSalle’s invariance principle, mathematical derivation of the mechanism is deduced rigorously to obtain the sufficient criteria in the form of linear matrix inequalities. Numerical simulations with the complex dynamical network composed of chaotic dynamical nodes are given to illustrate the effectiveness of our proposed method.

scholarly journals Topology Detection for Output-Coupling Weighted Complex Dynamical Networks with Coupling and Transmission Delays

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Xinwei Wang ◽  
Guo-Ping Jiang ◽  
Chunxia Fan ◽  
Xu Wu
Keyword(s):  
Dynamical System ◽  
Numerical Simulations ◽  
Invariance Principle ◽  
Time Delays ◽  
Complex Dynamical Networks ◽  
Output Coupling ◽  
Complex Dynamical Network ◽  
Dynamical Networks ◽  
Transmission Delays ◽  
Response System

Topology detection for output-coupling weighted complex dynamical networks with two types of time delays is investigated in this paper. Different from existing literatures, coupling delay and transmission delay are simultaneously taken into account in the output-coupling network. Based on the idea of the state observer, we build the drive-response system and apply LaSalle’s invariance principle to the error dynamical system of the drive-response system. Several convergent criteria are deduced in the form of algebraic inequalities. Some numerical simulations for the complex dynamical network, with node dynamics being chaotic, are given to verify the effectiveness of the proposed scheme.

scholarly journals A New Model for Complex Dynamical Networks Considering Random Data Loss

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 797
Author(s):  
Xu Wu ◽  
Guo-Ping Jiang ◽  
Xinwei Wang
Keyword(s):  
Network Model ◽  
Network Models ◽  
Lyapunov Stability Theory ◽  
Complex Dynamical Networks ◽  
Output Coupling ◽  
Data Loss ◽  
Complex Dynamical Network ◽  
Dynamical Networks ◽  
Dynamical Network ◽  
Proposed Model

Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.

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