Partial synchronization in stochastic dynamical networks with switching communication channels.

In this paper, the partial synchronization problem of stochastic dynamical networks (SDNs) is investigated. Unlike the existing models, the SDN considered in this paper suffers from a class of communication constraint-only part of nodes' states can be transmitted. Thus, less nodes' states can be used to synchronize the SDN, which makes the analysis of the synchronization problem much harder. A set of channel matrices are introduced to reflect such kind of constraint. Furthermore, due to unpredictable environmental changes, the channel matrices can switch among some communication modes. The switching considered here is governed by a Markov process. To overcome the difficulty, a regrouping method is employed to derive our main results. The obtained conditions guarantee that partial synchronization can be achieved for SDNs under switching communication constraint. Finally, numerical examples are given to illustrate the effectiveness of the theoretical results and how the communication constraint influences synchronization result.

[1]  Vicente Pérez-Muñuzuri,et al.  Autowaves for Image Processing on a Two-Dimensional CNN Array of Excitable Nonlinear Circuits: Flat and Wrinkled Labyrinths V. Perez-Mufiuzuri, V. Perez-Villar, and Leon 0. Chua, Fellow, ZEEE , 1993 .

[2]  F. Zou,et al.  Bifurcation and chaos in cellular neural networks , 1993 .

[3]  L. Chua,et al.  COEXISTENCE OF LOW- AND HIGH-DIMENSIONAL SPATIOTEMPORAL CHAOS IN A CHAIN OF DISSIPATIVELY COUPLED CHUA’S CIRCUITS , 1994 .

[4]  L. Chua,et al.  Synchronization in an array of linearly coupled dynamical systems , 1995 .

[5]  Kurths,et al.  Phase synchronization of chaotic oscillators. , 1996, Physical review letters.

[6]  T. Carroll,et al.  Master Stability Functions for Synchronized Coupled Systems , 1998 .

[7]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[8]  X. Mao Stability of stochastic differential equations with Markovian switching , 1999 .

[9]  Y. Lai,et al.  Observability of lag synchronization of coupled chaotic oscillators. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[11]  M. Young,et al.  The architecture of visual cortex and inferential processes in vision. , 2000, Spatial vision.

[12]  Belykh,et al.  Hierarchy and stability of partially synchronous oscillations of diffusively coupled dynamical systems , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[13]  Desmond J. Higham,et al.  An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations , 2001, SIAM Rev..

[14]  S. Strogatz Exploring complex networks , 2001, Nature.

[15]  H. Nijmeijer,et al.  Partial synchronization: from symmetry towards stability , 2002 .

[16]  Xiao Fan Wang,et al.  Synchronization in Small-World Dynamical Networks , 2002, Int. J. Bifurc. Chaos.

[17]  N F Rulkov,et al.  Generalized synchronization of chaos in noninvertible maps. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  James Lam,et al.  Nonlinear filtering for state delayed systems with Markovian switching , 2003, IEEE Trans. Signal Process..

[19]  M. Hasler,et al.  Persistent clusters in lattices of coupled nonidentical chaotic systems. , 2003, Chaos.

[20]  J. Buhmann,et al.  Influence of noise on the function of a “physiological” neural network , 1987, Biological Cybernetics.

[21]  Chai Wah Wu,et al.  Synchronization in arrays of coupled nonlinear systems with delay and nonreciprocal time-varying coupling , 2005, IEEE Transactions on Circuits and Systems II: Express Briefs.

[22]  C. Wu Synchronization in networks of nonlinear dynamical systems coupled via a directed graph , 2005 .

[23]  Jürgen Kurths,et al.  Structural and functional clusters of complex brain networks , 2006 .

[24]  Gang Zhang,et al.  A new method to realize cluster synchronization in connected chaotic networks. , 2006, Chaos.

[25]  M. Hasler,et al.  Synchronization in asymmetrically coupled networks with node balance. , 2006, Chaos.

[26]  Tianping Chen,et al.  New approach to synchronization analysis of linearly coupled ordinary differential systems , 2006 .

[27]  Changsong Zhou,et al.  Hierarchical organization unveiled by functional connectivity in complex brain networks. , 2006, Physical review letters.

[28]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[29]  Guanrong Chen,et al.  New criteria for synchronization stability of general complex dynamical networks with coupling delays , 2006 .

[30]  Katja Lindenberg,et al.  Continuous and discontinuous phase transitions and partial synchronization in stochastic three-state oscillators. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Jurgen Kurths,et al.  Synchronization in complex networks , 2008, 0805.2976.

[32]  Ligang Wu,et al.  Exponential stabilization of switched stochastic dynamical networks , 2009 .

[33]  Lixian Zhang,et al.  Stability and stabilization of Markovian jump linear systems with partly unknown transition probabilities , 2009, Autom..

[34]  Tianping Chen,et al.  Partial synchronization in linearly and symmetrically coupled ordinary differential systems , 2009 .

[35]  Daniel W. C. Ho,et al.  Pinning Stabilization of Linearly Coupled Stochastic Neural Networks via Minimum Number of Controllers , 2009, IEEE Transactions on Neural Networks.

[36]  Wei Wu,et al.  Cluster Synchronization of Linearly Coupled Complex Networks Under Pinning Control , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[37]  Jinde Cao,et al.  Cluster synchronization in an array of hybrid coupled neural networks with delay , 2009, Neural Networks.

[38]  Changsong Zhou,et al.  Graph analysis of cortical networks reveals complex anatomical communication substrate. , 2009, Chaos.

[39]  Jinde Cao,et al.  A unified synchronization criterion for impulsive dynamical networks , 2010, Autom..

[40]  Daniel W. C. Ho,et al.  Globally Exponential Synchronization and Synchronizability for General Dynamical Networks , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[41]  T. Pereira Hub synchronization in scale-free networks. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  Daniel W. C. Ho,et al.  Synchronization analysis of a complex network family , 2010 .

[43]  Zidong Wang,et al.  Global Synchronization for Discrete-Time Stochastic Complex Networks With Randomly Occurred Nonlinearities and Mixed Time Delays , 2010, IEEE Transactions on Neural Networks.

[44]  Zidong Wang,et al.  Exponential Stabilization of a Class of Stochastic System With Markovian Jump Parameters and Mode-Dependent Mixed Time-Delays , 2010, IEEE Transactions on Automatic Control.

[45]  Tianping Chen,et al.  Cluster synchronization in networks of coupled nonidentical dynamical systems. , 2009, Chaos.

[46]  Jinde Cao,et al.  Exponential Synchronization of Linearly Coupled Neural Networks With Impulsive Disturbances , 2011, IEEE Transactions on Neural Networks.

[47]  J. Kurths,et al.  Synchronization in networks of mobile oscillators. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Jie Zhang,et al.  Stabilization for Markovian jump systems with partial information on transition probability based on free-connection weighting matrices , 2011, Autom..

[49]  Jinde Cao,et al.  Exponential Stability of Stochastic Neural Networks With Both Markovian Jump Parameters and Mixed Time Delays , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[50]  Shouwei Zhao,et al.  A Lie algebraic condition for exponential stability of discrete hybrid systems and application to hybrid synchronization. , 2011, Chaos.

[51]  Jinde Cao,et al.  Synchronization Control for Nonlinear Stochastic Dynamical Networks: Pinning Impulsive Strategy , 2012, IEEE Transactions on Neural Networks and Learning Systems.