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A New Model for Complex Dynamical Networks Considering Random Data Loss
by
Wu, Xu
, Wang, Xinwei
, Jiang, Guo-Ping
in
Communication
/ Communications networks
/ complex dynamical network
/ Coupling
/ Data integrity
/ Data loss
/ Data transmission
/ Investigations
/ Lyapunov stability theory
/ Networks
/ random data loss
/ Random variables
/ Stability analysis
/ stochastic analysis method
2019
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A New Model for Complex Dynamical Networks Considering Random Data Loss
by
Wu, Xu
, Wang, Xinwei
, Jiang, Guo-Ping
in
Communication
/ Communications networks
/ complex dynamical network
/ Coupling
/ Data integrity
/ Data loss
/ Data transmission
/ Investigations
/ Lyapunov stability theory
/ Networks
/ random data loss
/ Random variables
/ Stability analysis
/ stochastic analysis method
2019
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Do you wish to request the book?
A New Model for Complex Dynamical Networks Considering Random Data Loss
by
Wu, Xu
, Wang, Xinwei
, Jiang, Guo-Ping
in
Communication
/ Communications networks
/ complex dynamical network
/ Coupling
/ Data integrity
/ Data loss
/ Data transmission
/ Investigations
/ Lyapunov stability theory
/ Networks
/ random data loss
/ Random variables
/ Stability analysis
/ stochastic analysis method
2019
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A New Model for Complex Dynamical Networks Considering Random Data Loss
Journal Article
A New Model for Complex Dynamical Networks Considering Random Data Loss
2019
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Overview
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.
Publisher
MDPI AG,MDPI
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