Synchronization of coupled connected neural networks with delays

By Wenlian Lu, Tianping Chen. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2004

433 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/57f7264ad36e4b7b6ca9d575d544374662398b24

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Guanrong Chen
2. Wenwu Yu
3. Jinde Cao
4. Zidong Wang
5. Jürgen Kurths
6. Xiaohui Liu
7. Jinhu Lu
8. Jinling Liang

In Statistical and Nonlinear Physics

1. Wenwu Yu
2. Guanrong Chen
3. Jinde Cao
4. Jinhu Lu
5. Zidong Wang
6. Jürgen Kurths
7. Xiaohui Liu
8. Tianping Chen

Citations in the corpus

• Pinning Complex Networks by a Single Controller (2006, methodology): “Using this distance, a methodology was proposed to discuss global convergence for complete regular coupling configuration (also see [4]).”
• Local Synchronization of a Complex Network Model (2009, methodology): “A lot of research papers [7]–[17], [37]–[39], [44], [45] have been published based on this same model, mainly on the concerned issue of synchronization.”
• Global Synchronization Control of General Delayed Discrete-Time Networks With Stochastic Coupling and Disturbances (2008, methodology): “A variational method has been used in [11] to deal with the synchronization problem for an array of linearly coupled identical connected neural networks with delays, whereas the similar problem has been addressed in [23] for an array of coupled nonlinear systems with delay and nonreciprocal timevary”
• Networked Synchronization Control of Coupled Dynamic Networks With Time-Varying Delay (2010, methodology): “In [3], a sufficient condition of global asymptotical synchronization for the coupled neural networks was derived by using the Hermitian matrix theory and Lyapunov functional method.”
• Consensus of Multiagent Systems and Synchronization of Complex Networks: A Unified Viewpoint (2016, background): “The approach of analysis for second order consensus of the model (15) can also be traced to the papers [3], [4].”
• Exponential synchronization of complex networks with Markovian jump and mixed delays (2008, neutral): “[11,17,19,23,24,38] and the references cited therein.”
• Global Synchronization of Linearly Hybrid Coupled Networks with Time-Varying Delay (2008, background): “In [6], Lu and Chen further investigated the synchronization of a coupled connected neural network with constant time-delay.”
• Consensus of Multi-Agent Systems With Unbounded Time-Varying Delays (2010, result): “As a direct consequence of the results obtained in the section of local synchronization of [ 8 ], we have ,when with . (For the general see [9]).”
• Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays (2008, methodology): “In recent years, the synchronization problem for various types of networks with delayed coupling has been extensively studied [2], [9], [17], [19], [23], [37].”
• Cluster Synchronization of Linearly Coupled Complex Networks Under Pinning Control (2009, background): “Therefore, the following question arose in [31]: Can we find relatively small coupling strength? Similarly, in the pinning process, it is also important to make the coupling strength as small as applicable.”
• Cluster Synchronization in Directed Networks Via Intermittent Pinning Control (2011, methodology): “For example, a distance from synchronization manifold to each state is defined in [13], and in [14]–[18], the left eigenvector corresponding to the zero eigenvalue of the diffusive coupling matrix is utilized to investigate the global synchronization.”
• Synchronization analysis of linearly coupled systems described by differential equations with a coupling delay (2006, background): “In [14–16], the authors investigated global synchronization of coupled nonlinear dynamical systems by introducing a distance to synchronization with some structural matrix.”
• Distributed Consensus Filtering in Sensor Networks (2009, methodology): “In [21], a distance from the collective states to the synchronization manifold was defined and then utilized to obtain conditions for global synchronization of coupled systems [12], [13], [22].”
• Synchronization via Pinning Control on General Complex Networks (2013, methodology): “For the global behavior, a distance function from the collective states of the network vertices to the synchronization manifold was used [40, 44] to study global synchronization of coupled networks and systems [18, 48, 49].”
• Global Synchronization in an Array of Delayed Neural Networks With Hybrid Coupling (2008, background): “In comparison, the synchronization criteria given in [26] and [27] require solving some nonlinear algebraic or matrix inequalities.”