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F. Dercole – Politecnico di Milano – ver. 28/01/2013 1/22 COMPLETE SYNCHRONIZATION OF NETWORKS OF DYNAMICAL SYSTEMS: Master-slave synchronization Synchronization via diffusion o Local stability of synchronization and the Master Stability Function (MSF) approach o Global stability of synchronization and the Connection Graph Stability (CGS) method o Comparison and extensions References and further reading

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  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 1/22

    COMPLETE SYNCHRONIZATION OF NETWORKS OF DYNAMICAL SYSTEMS:

    • Master-slave synchronization • Synchronization via diffusion

    o Local stability of synchronization and the Master Stability Function (MSF) approach

    o Global stability of synchronization and the Connection Graph Stability (CGS) method

    o Comparison and extensions

    • References and further reading

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 2/22

    MASTER-SLAVE SYNCHRONIZATION The master node represents an isolated oscillator (periodic or chaotic)

    )(wgw =& , mRw∈ , 2≥m that generates an input signal )(wu ϕ= .

    Slave nodes represent identical and non autonomous dynamical systems

    ),( )()( uxfx ii =& , ni Rx ∈)( , 2≥n , Ni ,,2,1 K= influenced by the input u .

    The unforced slave node )0,(xfx =& is an oscillator (periodic or chaotic).

    The n -dimensional synchronization (linear) manifold { })()2()1()()1( :,, NN xxxxx ====Σ LK

    is obviously invariant! The synchronous solution )()()()( )()2()1( txtxtxtx N ==== L , 0≥t , is a solution of the isolated ),( uxfx =& Given a non synchronous initial condition { })0(),0(,),0( )()2()1( Nxxx K (and any )0(w ), does the solution tend toward a synchronous solution?

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 3/22

    Consider a single master-slave pair, say slave node i : )(wgw =& , ))(,( )()( wxfx ii ϕ=&

    Let ⎥⎦

    ⎤⎢⎣

    ⎡= )(ix

    wz and ⎥

    ⎤⎢⎣

    ⎡=

    ))(,()(

    )( )( wxfwg

    zF i ϕ , so we have the autonomous system )(zFz =&

    The LE of the system are the LE of the master (LEM) together with the LE of the slave while forced by the master (LESM). These are the LE of the linear system

    )()()( ))(,( iii xwxJx δϕδ =& , xuxfuxJ ∂∂= /),(),( Note: LESM are (obviously) different from the LE of the unforced slave (LES), e.g., if the unforced system is chaotic, the largest LES is positive, but LESM can well be all negative!

    Main result: if all LESM are negative, then 0)()( →tx iδ from any small )0()(ixδ

    Consequence: any group of slaves with sufficiently similar initial condition synchronize.

    Indeed, L&& +−=−=− )())(,()))(,(())(,( )()()()()()()( ijiijij xxwxJwxfwxfxx ϕϕϕ

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 4/22

    Example: The “Moran” effect in ecology

    Can common environmental fluctuations cause the synchronization of isolated communities? Moran [1953]: isolated communities are described by identical stable linear systems

    uAxx ii += )()(& so that

    )( )()()()( ijij xxAxx −=− && i.e.

    0)()( →− ij xx from any initial condition and for any input u!

    Lynx fur returns in five regiond of Dungeness crabs catches at eight locations Northern Canada of the Pacific coast

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 5/22

    21 ww +

    1w2w

    3w

    The nonlinear explanation [Colombo et al. 2008 AN]:

    weather

    2121

    2121

    sup)(

    wwwwwwww

    wu+−+

    +−+== εϕ

    input

    )1(0 upp +=

    ),( )()( uxfx ii =&

    nodes

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 6/22

    Synchronization occurs if the largest LESM is negative, i.e if the populations do not contribute to chaos (no “biochaos”). Largest LESM

    Blue: negative Red: positive

    amplitude of input

    bio

    logic

    al p

    aram

    eter

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 7/22

    SYNCHRONIZATION VIA DIFFUSION

    The type of connection

    Isolated nodes represent identical and autonomous dynamical systems

    )( )()( ii xfx =& , ni Rx ∈)( , 2≥n , Ni ,,2,1 K=

    The isolated system )(xfx =& is an oscillator (periodic or chaotic).

    The network is undirected, unweighted and connected.

    The coupling is of diffusive (i.e. linear) nature, i.e.

    ∑∑==

    −+=1:

    )(

    1:

    )()()( )(ijij aj

    i

    aj

    jii xHdxHdxfx& ∑=

    −=N

    j

    jij

    i xHldxf1

    )()( )(

    where

    • ][ ijaA = and ][ ijlL = are the adiacency and laplacian ( NN × ) matrices • H is an nn× nonnegative matrix defining the diffusion profile • 0≥d modulates the coupling strength

    input output = d ki H x(i)

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 8/22

    The type of synchronization

    We consider the strongest (and simplest) case: complete synchronization. The n -dimensional synchronization (linear) manifold

    { }xxxxxx NN =====Σ )()2()1()()1( :,, LK is invariant, in fact

    )()(1

    )( xfxHldxfxN

    jij

    i ∑=

    =−=& (recall that L is zero row-sums!)

    is independent of i (because we consider identical dynamical systems!).

    Note: the synchronous solution )()()()( )()2()1( txtxtxtx N ==== L , 0≥t , is a solution of the isolated system )(xfx =& and is assumed to converge to a synchronous attractor A (periodic or chaotic). Note: this framework seems rather restricted, but there are many interesting applications (in physics, biology, ecology, mechanics,…see [Boccaletti et al. 2006 PR and refs therein]). There are also several extensions (see last page).

    )()( tx i

    )()( tx j

    Σ

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 9/22

    An example [Belykh et al. 2009 JBD]

    Each node represents a geographic location (island, patch) that is the habitat of a three-trophic food chain (resource R, consumer C, predator P).The isolated demographic dynamics are described with the classical Rosenzweig-MacArthur model:

    CRba

    RakRrRR

    11

    11

    1+

    −⎟⎠⎞

    ⎜⎝⎛ −=&

    PCba

    CaCdCRba

    RaeC22

    21

    11

    11 11 +

    −−+

    =&

    PdPCba

    CaeP 222

    22 1

    −+

    =&

    H is diagonal. It says which are the species that disperse and sets the relative dispersal rates, e.g.

    • [ ]001diagHH =′= , only R disperses (e.g. seeds transported by the wind) • [ ]010diagHH =′′= , only C disperses (e.g. planktonic feeders predated by trouts) • [ ]100diagHH =′′′= , only P disperses (e.g. herbivores)

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 10/22

    LOCAL STABILITY OF SYNCHRONIZATION AND THE MSF APPROACH Local stability and Lyapunov exponents (LEs)

    By linearizing around a synchronous chaotic solution )(tx , one can compute nN LEs, n of which are the exponents

    000 1

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 11/22

    The MSF approach [Pecora and Carroll 1998 PRL, Jansen and Lloyd 2000 JMB]

    It exploits the spectral properties of the laplacian matrix L • L is real and symmetric ⇒ real eigenvalues and diagonalizable

    (N linearly independent and orthogonal eigenvectors) [ ]iT diagTLT λ= , with T = [eigenvectors]T

    • L is zero row-sums ⇒ 0 is eigenvalue associated to the eigenvector [ ]T1,,1K • L− is Metzler (i.e. 0≥− ijl for ji ≠ ) and irreducible (⇒ Perron–Frobenius theory)

    ⇒ the dominant eigenvalue dλ is unique and real ⇒ 0 = min{row-sums} ≤≤ dλ max{row-sums} = 0 ⇒ 0=dλ

    Thus, the spectrum of L can be ordered as: Nλλλ ≤≤

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 12/22

    The MSF also exploits the following “tricky” change of coordinates:

    )()()( txx ii −=δ , Ni ,,2,1 K=

    The network model gets transformed into

    +−= )()()( ))(( iiii vHdvtxJv λ& h.o.t., Ni ,,2,1 K= ,

    where xfxJ ∂∂= /)( is the Jacobian of the isolated system.

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 13/22

    1=i ( 01 =λ ) ⇒ )1()1( ))(( vtxJv =&

    yields the LEs nLL ,,1 K of the synchronous attractor A

    Ni ,,2 K= ( 0>iλ ) ⇒ local stability of synchronization “requires” the largest LE of )()()( ))(( ii

    ii vHdvtxJv λ−=& to be negative

    Instead of the above )1( −N linear n-dimensional systems, we can discuss the

    Master Stability Equation (MSE): vHvtxJv ε−= ))((& , nRv∈ , 0≥ε and, in particular, draw the

    Master Stability Function (MSF): == )(εMSFMSF the largest LE of the MSE Notes:

    • 0)0( >MSF ( 0= ) if the synchronous attractor A is chaotic (periodic) • the MSF only depends on the isolated system (f ) and on the diffusion profile (H ),

    it is independent on the topology of the network. • drawing the MSF is computationally much heavier than computing 1−N of its values!

    local stability of synchronization “requires” 0)(

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 14/22

    Types of MSF

    type I synchronization is not possible on any network type II synchronization is possible on any network, provided

    2/λε sd > e.g.: sync is easier for large N on complete nets ( N=2λ ) sync is hard for large N on Watts-Strogatz loops ( 02 →λ )

    type III (= non type I or II) sync is possible (for suitable d) for networks with 12 ελ >d and 2ελ

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 15/22

    A “trivial” type II example

    H = I ⇒ εε −= )0()( MSFMSF

    A type III example

    MSFs for coupled Rössler oscillators

    bold/regular lines: chaotic/periodic regime solid lines: x1-coupling ⇒ type III dashed lines: x2-coupling ⇒ type II Small-world networks

    Note: sync is favored by rewiring/adding (for type III MSF 2/λλN drops by increasing p).

    ε

    02 =− λN = 500 p = 0.1

    1ε 2εMSF

    ε

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 16/22

    The ecological example

    (I) [ ]001diagHH =′= (II) [ ]010diagHH =′′= (III) [ ]100diagHH =′′′= Note: sync is favored if intermediate trophic levels diffuse more than bottom/top levels.

    MSF

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 17/22

    GLOBAL STABILITY OF SYNCHRONIZATION AND THE CGS METHOD Note: obviously global stability implies local stability. A “strong” assumption

    Given the isolated system (f ) and the type of coupling (H ), we assume that the network of two coupled systems “globally synchronizes” for all 02 >> dd . Notes:

    • it is a sort of “MSF-type-II” assumption • this must be shown for each given pair (f , H ) • it is the most difficult step, but can be easily tested numerically!

    The ecological example [Belykh et al. 2009 JBD, Appendix]

    • dispersion profile [ ]111diagH = (the analysis is similar for other profiles) • boundedness of solutions: it can be shown that the region of nonnegative

    )2,1(R , )2,1(C , )2,1(P bounded by the plane 0)2()2()2()1()1()1( =−+++++ cPCRPCR is an absorbing domain for L=> *cc

    • Lyapunov function: ( ) 8/)()()( 2)1()2(2)1()2(2)1()2( PPCCRR −+−+−=Φ By exploiting the above bound, it can be shown that 0 2dd

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 18/22

    Notes:

    • the EIG condition is similar to the MSF type-II condition ( 2/λε sd > ) • CGS avoids spectra computations and directly links sync with network topology

    • the choice of ijP ’s is not unique; shortest paths do not always yield the best bound • overloaded links (involved by many ijP ’s) make sync difficult (high kz ) • adding a link with constant N can only make sync easier • the computation of kk zmax for various network topologies can be found in

    [Belykh et al. 2004 PD, 2005 CHAOS, 2005 IJBC, 2006 PD]

    The eigenvalue (EIG) method [Wu and Chua 1996 I3ESC-I]

    Synchronization is globally stable if 22 /2 λdd >

    The CGS method [Belykh et al. 2004 PD]

    • select one path ijP from each node i to each node j ( ji < ) • compute the sum kz of the lengths of all paths containing link k , mk ,,1K=

    Synchronization is globally stable if kmk

    zNdd

    ≤≤>

    12 max2

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 19/22

    Sketch of the proof (for diagonal H)

    • Let 2/)()( )1()2()1()2(2 xxVxxVT −−=

    be the Lyapunov function used to show global sync in the two-node net

    • Consider the Lyapunov function ∑∑−

    = >

    −−=1

    1

    )()()()( )()(21 N

    i ij

    ijTijN xxVxxV for the network model

    • It can be shown that NV& is negative definite if, for all ∑∑ == ==Ni

    ii

    Ni

    iip wvX 2

    )(2

    )( βα , )()( iii vLv λ=

    ∑∑∑−

    = >=

    −>−1

    1

    2)()(2

    1

    2)()( )(2)(N

    i ij

    ip

    jp

    m

    k

    ip

    jp xxN

    dxxd kk , np ,,1K=

    • Variables )( )()( ipj

    p xx − can be eliminated in the above inequality by substituting

    ∑∈

    −=−ij

    kk

    Pk

    ip

    jp

    ip

    jp xxxx )(

    )()()()( (sort of “Kirchhoff” rule)

    and by bounding 2)()(2)()( )())(( ∑∑

    ∈∈−≤−

    ij

    kk

    ij

    kk

    Pk

    ip

    jpij

    Pk

    ip

    jp xxPxx (Cauchy-Schwarz inequality)

    • Collecting terms yields kmk

    zNdd

    ≤≤>

    12 max2

    22 pp

    Tp XXLX λ≥=

    2pXN=

    )()( ii wNFw =it results:

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 20/22

    The ecological example

    Synchronization is globally stable if 22 39

    62 ddd =>

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 21/22

    COMPARISON AND EXTENSIONS General conclusion: synchronization (when possible) requires

    • large coupling when connections are local (small 2λ , large kk zmax ) • small coupling when connections are global (large 2λ , small kk zmax )

    MSF / CGS comparison and extensions

    MSF Pros

    • almost “iff” condition • easy to apply • also nonlinear (diffusive) coupling

    Cons

    • only local stability • less extensions available

    directed and weighted nets – slightly nonidentical oscillators

    CGS Pros

    • global stability • more extensions available

    directed and weighted nets – time-varying coupling – slightly nonidentical oscillators

    Cons

    • conservative “if” condition • can be difficult to apply • only linear (diffusive) coupling

  • F. Dercole – Politecnico di Milano – ver. 28/01/2013 22/22

    REFERENCES AND FURTHER READING [Belykh et al. 2004 PD] V.N. Belykh, I. Belykh, and M. Hasler (2004) Connection graph stability method for synchronized coupled chaotic systems, Phys. D 195, 159–187.

    [Belykh et al. 2005 CHAOS] I. Belykh, V.N. Belykh, and M. Hasler (2006) Synchronization in asymmetrically coupled networks with node balance, Chaos 16, 015102.

    [Belykh et al. 2005 IJBC] I. Belykh, M. Hasler, M. Lauret, and H. Nijmeijer (2005) Synchronization and graph topology, Int. J. Bifurc. Chaos 11, 3423–3433.

    [Belykh et al. 2006 PD] I. Belykh, V.N. Belykh, and M. Hasler (2006) Generalized connection graph method for synchronization in asymmetrical networks, Phys. D 224, 42–51.

    [Belykh et al. 2009 JBD] I. Belykh, C. Piccardi, and S. Rinaldi (2009) Synchrony in tritrophic food chain metacommunities. J. Biol. Dynamics 3, 497–514.

    [Boccaletti et al. 2006 PR] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang (2006) Complex networks: structure and dynamics, Phys. Rep. 424, 175–308.

    [Colombo et al. 2008 AN] A. Colombo, F. Dercole, and S. Rinaldi (2008) Remarks on metacommunities synchronization with application to prey-predator systems, Am. Nat. 171, 430–442.

    [Jansen and Lloyd 2000 JMB] V.A.A. Jansen and A. L. Lloyd (2000) Local stability analysis of spatially homogeneous solutions for multi-patch systems. J. Math. Biol. 41,232–252.

    [Pecora and Carroll 1998 PRL] L.M. Pecora and T.L. Carroll (1998) Master stability functions for synchronized coupled systems. Phus. Rev. Lett. 80, 2109–2112.

    [Sun et al. 2009 EPL] J. Sun, E.M. Bollt, and T. Nishikawa (2009) Master stability functions for coupled nearly identical dynamical systems. Europhys. Lett. 85, 60011.

    [Wu and Chua 1996 I3ESC-I] C.W. Wu and L.O. Chua (1996) On a conjecture regarding the synchronization in an array of linearly couplet dynamical systems. IEEE Trans. Circuits Syst.-I 43, 161–165.