Dynamical Systems and Information Theory

Transcription

1 Dynamcal Systems and Informaton Theory Informaton Theory Lecture 4 Let s consder systems that evolve wth tme x F ( x, x, x,... That s, systems that can be descrbed as the evoluton of a set of state varables Such evoluton can be n dscrete or contnuous The former s governed by dfference or recurrence equatons, the later by dfferental equatons t+ t t t or n d x F n n dt ( x Some Vocabulary Dfferental equatons n frst-order form If F s lnear, the system s a lnear system, lkewse nonlnear The order of the system s the number of hstorcal terms n the dfference equatons, or the hghest order n n the dfferental equatons (,,,... x F x x x t+ t t t or n d x F n n dt ( x In general, a system of dfferental equatons can be converted to a frst order system through the addton of varables Here s an example for a second order, lnear d x dx m + b + kx dt dt d x b dx k + x dt m dt m x x dx dt dx x m m x& Ax system dt ( k ( b Egenvalues and Egenvectors Egen s a German word, whch roughly translates to characterstc For a mathematcal transformaton of some vector of varables An egenvector of the transformaton s a characterstc shape for that transformaton An egenvalue s a correspondng magntude for that shape A transformaton may have several egenvalues and egenvectors Representng behavors of transformatons as a combnaton of egenvectors s a form of data compresson We wll examne egenvalues and vectors n contnuous dynamcal systems as an example An example Consder solvng a ordnary, lnear dfferental equaton We solve by assumng a soluton form Whch reduces to the problem of fndng egenvectors mx && + bx& + kx x Ce x& x t x&& x + + m x b x kx gnorng the trval x soluton + + m b k 4 m b± b mk

2 In frst-order form In dynamcal systems Ths s the standard egenvalue problem for A Solutons are the egenvalues for the matrx (transformaton A For a gven, the soluton for x n xax s an egenvector x& Ax x c e t x Ax gnorng the trval x soluton A I A I Egenvectors (shapes represent modes of the characterstc (unforced behavor of the system Egenvalues (magntudes are related to these shape s duratons through tme Behold the wonder of Euler In summary Egenvalues come n complex conjugate pars Thus postve real parts ndcate growth negatve real parts ndcate decay Imagnary parts ndcate frequency of oscllaton Of the assocated egenvector (shape t e cost+ sn t ( r+ t rt ( r± t rt ( cos sn e e t+ t for complex conjugate pars ( cos e e t For a transformaton, egenvectors are characterstc shapes, egenvalues of ther characterstc magntudes For dynamcal systems, these the duratons through tme of modes of behavor We can descrbe contnuous lnear dynamcal systems wth a matrx, va frst order form Egenvectors of ths matrx ndcate one of several characterstc shapes of a dynamcal systems evoluton For correspondng egenvalues: Postve real parts ndcate that shape grows exponentally Negatve real parts ndcate that shape des off exponentally Imagnary parts ndcate the speed of oscllaton around that shape ( natural frequency Attractors Three knds of attractors In general, we can say that dynamcal systems have transent behavor (that whch des out over tme and steady-state behavor Any steady state behavor s also known as an attractor of that system Systems can also dverge (one of more of ther state varables can go to nfnty Fxed ponts An equlbrum value of the state vector Perodc attractors A repeatng sequence of state vector values Chaotc attractors A sequence that never dverges, but never repeats (!? Attractors can also be stable or unstable

3 s Examnng attractors Sngular value decomposton As an experment, let s construct a matrx descrbng a dynamcal systems behavor usng the method of delays Ths method allows s a non-analytcal way of examnng system behavor wthout havng to have the system equatons We can treat ether dscrete or contnuous systems wth ths method [... ] X x x x x t t t tm Is a generalzaton of egen decomposton (whch we ll talk about n more detal later Let s get the sngular values of X Then normalze them to - The dstrbuton ndcates the complexty of system dynamcs Let s take the entropy of the resultng dstrbuton ' H j H j log ' ' An Example Low Let s consder a set of partcles connected wth nonlnear sprngs and dampers We can thnk of ths as a sort of partcle swarm Let s look at how vares wth the sprng and damper strength log( 3 4 Moton n ths fgure s largely rght to left Ths s the case where the long term behavor s for the partcles to lock and behave lke a sngle partcle Relatve to the partcle s center of mass, ths s a fxed pont y poston x poston Medum Symbolc Dynamcs Is the stuaton where the partcles do not dverge, but do not coalesce It s lkely that ths s a chaotc attractor (but I haven t techncally proven that We mght call the behavor complex, emergent or self organzed We ll look a bt more at complexty measures y poston x poston Let s assume that we are takng measurements of a dynamcal system n dscrete tme, and that each measurement results n one symbol from an alphabet A, consstng of k possble symbols The underlyng system mght be a dscrete or contnuous dynamcal system Wth or wthout stochastc elements Note that we are brushng over detals of stochastc processes at ths pont 3

4 Let s consder a symbolc dynamcal system (Crutchfeld and Shalz Generatng a sequence of symbols S - S - S S S For a gven tme t, we wll label the past and future sequences And we defne the noton of a statonary stochastc process, f the probablty of any measurable future event sequence (taken from the possble set F s ndependent of tme s St s the past r St s the future the system s statonary r s f P S A S s ( t t r s ( t t P S A S s for all t and t s L S are the last L symbols r L S are the next L symbols Predctng the future We want to look at prevous symbols, and predct the probablty dstrbuton of future symbol sequences We are gong to partton the set of possble prevous symbols such that all the elements n a gven cell of ths partton are matched to the same predcted dstrbuton over the set of possble future sequences If the functon mappng a past hstory to a future dstrbuton s, past sequences s and s, are n the same partton cell f and only f (s (s Effectve states Learnng We wll call each cell n ths partton an effectve state of the underlyng process, for a gven predcton functon We wll call R the set of effectve states nduced by We would lke to learn the partton, and the predcted dstrbutons, based on past sequences Let s concentrate on gettng the rght parttons We d lke to maxmze the mutual nformaton between the partton R and the possble sequences of future states Any predcton that s as good as one could do rememberng all past states s called prescent r r r I S R H S H S R L L L ( ; ( + ( r r s L L ( ( H S R H S S Statstcal Complexty Causal states C(R s the number of bts needed to represent the partton Note that whle ths s computed n bts, and s based on a statstcal model, t s a dfferent sort of complexty measure than H It s a sort of machne sze We wll call the (unque set of prescent states that mnmzes statstcal complexty the causal states of the system Let s recap: ths s the most effcent set of sets of prevous symbols that predct the probablty dstrbuton of future sequences 4

5 But there s more The system s -machne Gven one causal state, and a symbol from the real process, we move to another causal state We want to fnd those transtons, as well It turns out that ths gves a determnstc dynamcal system n the followng sense For a causal state, and current symbol s, the machne moves to another partcular causal state, wth probablty However, recall the system we are modelng s stochastc, so the model s stochastc, n the sense that the sequence of symbols s that are nput s stochastc Also recall that the causal states are mapped to probablty dstrbutons over the future states by the functon Whew! Is defned by the symbol set of the orgnal symbolc dynamcal system, that system s causal states, and the transton probablty matrces T (s r T P S s S S (, + ( s ' ' j t j t Markov Process The causal states form a Markov process That s you only need to know the current state to completely determne the probablty dstrbuton over all possble future states We call also ths the Markov property Recurrent, Transent, and Synchronzaton States In a Markov process, states are ether Recurrent vsted over and over agan n an nfnte loop Transent vsted once, and never returned to agan In an -machne, transent states are also called synchronzaton states snce the represent the hstory of symbols you have to see before you can fx yourself nto the approprate recurrent state Crutchfeld s complexty measures wll gnore synchronzaton states, n general We mght also call a set of connected recurrent states and attractor of the process Complexty metrcs Two knds of predctable We need two numbers to characterze the complexty of the system, gven the -machne C(R, the statstcal complexty The varable memory needed to represent the machne H, the entropy of the state transtons Ths s rather profound! Weather that s wldly varable s predctable n ts varablty (hgh H Well treated wth probablstc models Weather that s very perodc s very predctable (hgh C Well treated wth determnstc models Complex weather s nether of these thngs (complexty n ths sense s characterzed by bounded randomness and relatvely hgh sze of the machne used to descrbe dynamcs Hard to get a good model of ether knd 5

6 Causal state splttng reconstructon (CSSR A somewhat exhaustve algorthm for fndng a system s -machne We start by assumng only one causal state, and the largest possble It s very nterestng to look at the complexty metrcs nferred for varous systems The CSSR algorthm Gven data from a system of symbol dynamcs Start wth one causal state and the assumpton that symbols are unformly randomly generated (maxmum H Test statstcally to see f causal states should be added If so, add a state, and compute approprate dstrbutons and transton probabltes from the gven data, and repeat If not, stop Slghtly more detal If Set L, S { } (the null causal state Whle L<L max For each causal state n S Calculate the condtonal probablty dstrbuton of all future state sequences of length L For each hstory n Consder each sequence that conssts of ths hstory and one more prevous character Calculate the condtonal probablty dstrbuton of all future state sequences of length L Use a statstcal test to see f ths dstrbuton s the same as that for any exstng causal state The new hstory gves a dstrbuton that s statstcally the same as that of an exstng causal state Add ths hstory to that state Else Create a new state that contans just ths hstory Calculate the causal state transtons correspondng to any gven symbol I have smplfed ths terrbly! A CSSR Example CSSR gves an -machne Consder the famous logstc equaton X(t+rX(t(-X(t Ths s the prmary example of determnstc chaos We convert t to a symbolc dynamcal system by outputtng f X(t>.5, otherwse For each value of r, and L max 6 These are plotted n the space of the two complexty measures C ( machne sze and H ( randomness The phase transton occurs at the Fegenbaum number 6

7 At the phase transton The Edge of Chaos Addng more nference to CSSR (ncreasng L max just leads to larger and larger machne sze (V s approxmately C Ths s the so-called edge of chaos It also ndcates a jump up Chomsky s herarchy of grammars Is a phenomena often dscussed n the feld of Complexty It seems to ndcate an regon of system dynamcs bounded by smple and smply random behavors, where Interestng developmental or accdental patterns and phenomena occur n the system It s what I was tryng to capture wth medum Another study of the edge Attractor Length Consder Kaufman s Random Boolean Networks Recurrent networks (dynamcal systems wth bnary outputs/nputs, and random Boolean functons at the nodes Characterzed by N (number of nodes and K (connectvty Started wth some bt strng, they settle towards one of (possbly many attractors N... F(x F(x F(x F(x K... As a functon of N and K For K < 3 (sh, length of attractors expands as sqrt(n For K > 5 (sh, length of attractors expands exponentally wth N For K around 3 length of attractors s sublnear n N Number of dstnct attractors Stablty of attractors As a functon of N and K For K < 3 (sh, number of attractors expands exponentally wth N For K > 5 (sh, number of attractors expands as a low-order polynomal of N For K around 3 number of attractors expands sub-lnearly n N That s, whether small random perturbatons return to a gven attractor, or go to some other attractor For N<3 (sh attractors are farly unstable For N>5 attractors unstable For N around 3, attractors are stable 7

8 Dmensonless Input (NK Entropy L6 L8 L4 L Summary of ths edge K<3: many smple unstable behavors K>5: few complcated unstable behavors K around 3: few medum complcated stable behavors Ths s another edge of chaos But s t the same one Untng Crutchfeld and Kaufman s Edges? Procedure Generate large numbers of RBNs, wth varous levels of ongong perturbaton (mutatons of the output Use CSSR to fnd -machnes for the results Fnd a unfed method of examnng the results Dmensonless Entropy Consder H/C, the random complexty relatve to the machne complexty We examne ths for the nput and the output of the RBNs: At the nput, C s the number of bts necessary to descrbe the RBN, and H s the entropy of the mutatons At the output, C and H are as gven by CSSR We are measurng the complexty of what we can nfer, versus what s actually there.9 Prelmnary Results Dmensonless Output (Epslon Machne Entropy Dmensonless Output (Epslon Machne Entropy Dmensonless Input (NK Entropy K K K3 K4 K5 K6 K7 K8 Take Home Messages Dynamcal system (ncludng symbolc dynamcs behavor can be characterzed by (compressed nto Egen decomposton (and smlar Attractor descrpton And n a broader sense, nformaton theoretc approaches Whch can be characterzed by Markov chans Such examnaton reveals, among other thngs Two dstnct knds of complexty: randomness and machne sze The edge of chaos phenomena These reman actve research topcs 8