Complex Dynamics

Total Page:16

File Type:pdf, Size:1020Kb

Complex Dynamics Chapter 8 Complex Dynamics In the last part of this course we study the dynamics of complex maps f : C → C. Here we find the richness of dynamics in R2, but in a context where we can prove much more, thanks to the powerful and beautiful machinery of complex analysis. The history of the subject is remarkable. In the decade following the end of the First World War in 1918 the French mathematicians Pierre Fatou and Gaston Julia developed the theory of iterated rational maps (maps of the form z → P (z)/Q(z) where P and Q are polynomials). They did so independently and very much in competition, proving a very impressive body of results but leaving unsolved some central questions. The subject went into a quiet period - as often happens in mathematics after a wave of new development - for about 50 years, until around 1980 a number of leading present day mathematicians (Mandelbrot, Douady, Hubbard, Sullivan, Milnor, Thurston) began taking up the questions where Fatou and Julia had left off, with inspiration provided by the extraordinarily beautiful pictures produced by computer graphics. The subject has given birth to to remarkable pictures and to remarkable mathematics. Moreover, as we shall see, this mathematics tells us much more about the real one-dimensional maps we looked at earlier in the courses - in particular the logistic family, period-doubling etc. We first recall some basic facts and theorems from complex analysis. Definitions A map f : C → C is said to be differentiable at z0 if f(z) − f(z ) lim 0 z→z0 z − z0 0 exists, in which case we call the value of this limit the derivative f (z0) of f at z0. f is said to be holomorphic at z0 if f is differentiable at every point z in some neighbourhood of z0 (i.e. there exists a real number r > 0 such that f is differentiable at every z such that |z − z0| < r). f is said to be analytic at z0 if there exist a real number r > 0 and complex numbers an (1 ≤ n < ∞) such that X n f(z) = an(z − z0) ∀z with |z − z0| < r n=0 1 Key Property (which can be proved using Cauchy’s theorem): A map f : C → C is analytic at z0 ⇔ f is holomorphic at z0. Being analytic is a very strong property indeed: it says that once we know the Taylor series of f at z0 we know the exact value of f(z) at every point z inside the disc of convergence of the Taylor series - the remainder term that we have in the real form of Taylor’s Theorem is zero in the complex case. 0 Geometrical interpretation of f (z0) 0 If f is differentiable at z0, with derivative f (z0), then, to a first approximation 0 f(z) − f(z0) = (z − z0)f (z0) So, to a first approximation, f acts on a small round disc centred on z0 by expanding it by a scale factor 0 0 |f (z0)|, turning it through an angle arg(f (z0)), and centring the resulting disc on f(z0). 0 Note that it follows that f is conformal (angle-preserving) at z0 if f (z0) 6= 0. If f is differentiable at z0 and z0 is a fixed point of f, then it is self-evident from this geometrical picture that: 0 z0 is an attractor if |f (z0)| < 1 0 z0 is a repeller if |f (z0)| > 1 0 z0 is neutral if |f (z0)| = 1 Example: f(z) = z2 − 1 √ √ √ This has fixed points z = (1 ± 5)/2. The derivative of f is 2z. Since both |1 + 5| > 1 and |1 − 5| > 1 both fixed points are repellers. If we are concerned with a periodic orbit z0 → z1 → z2 → ... → zn = z0 then, to a first approximation, 0 0 the first map is (locally) multiplication by f (z0), and the second is multiplication by f (z1) etc. Hence the n 0 0 0 composite f is (locally) multiplication by the product f (z0)f (z1) . f (zn−1) = ζ, called the multiplier n n of the orbit. (Note that by the chain rule ζ is the derivative of f at z0, or indeed the derivative of f at any other point of the cycle {z0, . , zn−1}.) This period cycle is: attracting if |ζ| < 1 repelling if |ζ| > 1 neutral if |ζ| = 1 2 Example: f(z) = z2 − 1 The period two cycle 0 → −1 → 0 is an attractor since it has multiplier 0 × (−2) = 0. An attractor with multiplier 0 is called a superattractor. We shall concentrate on polynomial maps, and in particular on quadratic maps 2 f(z) = αz + βz + γ (α, β, γ ∈ C, α 6= 0) This looks like a 3-parameter family of maps, but we can reduce the number of parameters if we recall that conjugate maps have the same dynamics. Definition We say that analytic maps f and g from C to C are conjugate if there exists an analytic bijection h : C → C, with analytic inverse, such that g = hfh−1. Fact (from complex analysis): h : C → C is an analytic map, with an analytic inverse, if and only if h has the form h(z) = kz + l for some k, l ∈ C with k 6= 0. Proposition Every quadratic map f(z) = αz2 + βz + γ (α, β, γ ∈ C, α 6= 0) is conjugate to a unique map of the form 2 qc(z) = z + c (c ∈ C). Proof We try to find a map h(z) = kz + l such that hf(z) = qch(z)∀z ∈ C. hf(z) = k(αz2 + βz + γ) + l 2 qch(z) = (kz + l) + c 2 2 Equating coefficients of z ,z and 1 in these expressions we see that hf = qch if and only if kα = k , kβ = 2kl and kγ + l = l2 + c. Thus for a given α 6= 0, β and γ we may take k = α, l = β/2 and get a conjugacy h(z) = kz + l from f to 2 qc where c = αγ + β/2 − β /4. Indeed since we ask for h to be an analytic bijection with analytic inverse it must have the form h(z) = kz + l, so for a given α 6= 0, β and γ we obtain a unique value of c ∈ C. Remark The same proof goes through in the real case to show that every x → αx2 + βx + γ with α 6= 0, β, γ ∈ R is conjugate to some x → x2 + c with c ∈ R, but we can no longer deduce immediately that the value of c is unique, since there are many diffeomorphisms of R which are not of the form x → kx + l. 3 We have just shown that every quadratic polynomial is conjugate to one of the form z → z2 + c. Equally well we can prove that every quadratic polynomial is conjugate to one in the logistic family fµ(z) = µz(1 − z) (where now µ ∈ C): Proposition 2 qc is conjugate to fµ ⇔ c = µ/2 − µ /4. Proof This can be proved from first principles (like the previous proposition), or more simply by applying the previous proposition with α = −µ, β = µ and γ = 0. Note that for most values of c there are two values of µ with fµ conjugate to qc, the exception being c = 1/4, for which there is just one value of µ, namely µ = 1. Corollary (of the proposition immediately above) 2 qc has an attracting fixed point if and only if c = µ/2 − µ /4 for some µ with |µ| < 1. The fixed points of fµ are 0 and 1−1/µ. These have multipliers µ and µ−2µ(1−1/µ) = 2−µ respectively. 2 So qc has an attracting fixed point if and only if c = µ−µ /4 for some µ with either |µ| < 1 or |2−µ| < 1. 2 2 2 But µ/2 − µ /4 = (2 − µ)/2 − (2 − µ) /4. So qc has an attracting fixed point if and only if c = µ/2 − µ /4 for some µ with |µ| < 1. The set {c ∈ C : c = µ/2 − µ2/4 for some µ with |µ| < 1} is a cardioid, which intersects the real axis in the open interval (−3/4, 1/4). 2 Dynamics of qc : z → z + c 2 When c = 0 this is the map q0 : z → z . In this case initial points z0 with |z0| < 1 have orbits tending to the super-attracting fixed point 0. Initial points z0 with |z0| > 1 have orbits tending to ∞ (which can also be thought of as a super-attracting fixed point, as we shall see). On the unit circle the map q0 behaves as the doubling map (the binary shift) t → 2t mod 1 (where arg(z) = 2πt, i.e. z = e2πit), discussed earlier in the course. Thus on the unit circle q0 has sensitive dependence on initial conditions, and on this circle there is a sense collection of repelling periodic orbits (constructed earlier in the course): m 2nt = t mod 1 ⇔ 2nt − t = m some m ∈ ⇔ t = Z 2n − 1 4 The point 0 is a critical point for q0 (that it, a point where the derivative of q0 is zero). Earlier we saw the geometrical interpretation of a non-zero derivative. We now consider the geometrical interpretation 0 of the fact that q0(0) = 0. A small disc of radius r around z = 0 is mapped by q0 to a much smaller disc, of radius r2.
Recommended publications
  • An Image Cryptography Using Henon Map and Arnold Cat Map
    International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072 An Image Cryptography using Henon Map and Arnold Cat Map. Pranjali Sankhe1, Shruti Pimple2, Surabhi Singh3, Anita Lahane4 1,2,3 UG Student VIII SEM, B.E., Computer Engg., RGIT, Mumbai, India 4Assistant Professor, Department of Computer Engg., RGIT, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this digital world i.e. the transmission of non- 2. METHODOLOGY physical data that has been encoded digitally for the purpose of storage Security is a continuous process via which data can 2.1 HENON MAP be secured from several active and passive attacks. Encryption technique protects the confidentiality of a message or 1. The Henon map is a discrete time dynamic system information which is in the form of multimedia (text, image, introduces by michel henon. and video).In this paper, a new symmetric image encryption 2. The map depends on two parameters, a and b, which algorithm is proposed based on Henon’s chaotic system with for the classical Henon map have values of a = 1.4 and byte sequences applied with a novel approach of pixel shuffling b = 0.3. For the classical values the Henon map is of an image which results in an effective and efficient chaotic. For other values of a and b the map may be encryption of images. The Arnold Cat Map is a discrete system chaotic, intermittent, or converge to a periodic orbit. that stretches and folds its trajectories in phase space. Cryptography is the process of encryption and decryption of 3.
    [Show full text]
  • The Transition Between the Complex Quadratic Map and the Hénon Map
    The Transition Between the Complex Quadratic Map and the Hénon Map by Sarah N. Kabes Technical Report Department of Mathematics and Statistics University of Minnesota Duluth, MN 55812 July 2012 The Transition Between the Complex Quadratic Map and the Hénon map A PROJECT SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Sarah Kabes in partial fulfillment of the requirements for the degree of Master of Science July 2012 Acknowledgements Thank you first and foremost to my advisor Dr. Bruce Peckham. Your patience, encouragement, excitement, and support while teaching have made this experience not only possible but enjoyable as well. Additional thanks to my committee members, Dr. Marshall Hampton and Dr. John Pastor for reading and providing suggestions for this project. Furthermore, without the additional assistance from two individuals my project would not be as complete as it is today. Thank you Dr. Harlan Stech for finding the critical value , and thank you Scot Halverson for working with me and the open source code to produce the movie. Of course none of this would be possibly without the continued support of my family and friends. Thank you all for believing in me. i Abstract This paper investigates the transition between two well known dynamical systems in the plane, the complex quadratic map and the Hénon map. Using bifurcation theory, an analysis of the dynamical changes the family of maps undergoes as we follow a “homotopy” from one map to the other is presented. Along with locating common local bifurcations, an additional un-familiar bifurcation at infinity is discussed.
    [Show full text]
  • Quasiconformal Mappings, Complex Dynamics and Moduli Spaces
    Quasiconformal mappings, complex dynamics and moduli spaces Alexey Glutsyuk April 4, 2017 Lecture notes of a course at the HSE, Spring semester, 2017 Contents 1 Almost complex structures, integrability, quasiconformal mappings 2 1.1 Almost complex structures and quasiconformal mappings. Main theorems . 2 1.2 The Beltrami Equation. Dependence of the quasiconformal homeomorphism on parameter . 4 2 Complex dynamics 5 2.1 Normal families. Montel Theorem . 6 2.2 Rational dynamics: basic theory . 6 2.3 Local dynamics at neutral periodic points and periodic components of the Fatou set . 9 2.4 Critical orbits. Upper bound of the number of non-repelling periodic orbits . 12 2.5 Density of repelling periodic points in the Julia set . 15 2.6 Sullivan No Wandering Domain Theorem . 15 2.7 Hyperbolicity. Fatou Conjecture. The Mandelbrot set . 18 2.8 J-stability and structural stability: main theorems and conjecture . 21 2.9 Holomorphic motions . 22 2.10 Quasiconformality: different definitions. Proof of Lemma 2.78 . 24 2.11 Characterization and density of J-stability . 25 2.12 Characterization and density of structural stability . 27 2.13 Proof of Theorem 2.90 . 32 2.14 On structural stability in the class of quadratic polynomials and Quadratic Fatou Conjecture . 32 2.15 Structural stability, invariant line fields and Teichm¨ullerspaces: general case 34 3 Kleinian groups 37 The classical Poincar´e{Koebe Uniformization theorem states that each simply connected Riemann surface is conformally equivalent to either the Riemann sphere, or complex line C, or unit disk D1. The quasiconformal mapping theory created by M.A.Lavrentiev and C.
    [Show full text]
  • On the Combinatorics of External Rays in the Dynamics of the Complex Henon
    ON THE COMBINATORICS OF EXTERNAL RAYS IN THE DYNAMICS OF THE COMPLEX HENON MAP. A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Ricardo Antonio Oliva May 1998 c Ricardo Antonio Oliva 1998 ALL RIGHTS RESERVED Addendum. This is a slightly revised version of my doctoral thesis: some typing and spelling mistakes have been corrected and a few sentences have been re-worded for better legibility (particularly in section 4.3). Also, to create a nicer pdf document with hyperref, the title of section 3.3.2 has been made shorter. The original title was A model for a map with an attracting fixed point as well as a period-3 sink: the (3-1)-graph. ON THE COMBINATORICS OF EXTERNAL RAYS IN THE DYNAMICS OF THE COMPLEX HENON MAP. Ricardo Antonio Oliva , Ph.D. Cornell University 1998 We present combinatorial models that describe quotients of the solenoid arising from the dynamics of the complex H´enon map 2 2 2 fa,c : C → C , (x, y) → (x + c − ay, x). These models encode identifications of external rays for specific mappings in the H´enon family. We investigate the structure of a region of parameter space in R2 empirically, using computational tools we developed for this study. We give a combi- natorial description of bifurcations arising from changes in the set of identifications of external rays. Our techniques enable us to detect, predict, and locate bifurca- tion curves in parameter space. We describe a specific family of bifurcations in a region of real parameter space for which the mappings were expected to have sim- ple dynamics.
    [Show full text]
  • Chaos: the Mathematics Behind the Butterfly Effect
    Chaos: The Mathematics Behind the Butterfly E↵ect James Manning Advisor: Jan Holly Colby College Mathematics Spring, 2017 1 1. Introduction A butterfly flaps its wings, and a hurricane hits somewhere many miles away. Can these two events possibly be related? This is an adage known to many but understood by few. That fact is based on the difficulty of the mathematics behind the adage. Now, it must be stated that, in fact, the flapping of a butterfly’s wings is not actually known to be the reason for any natural disasters, but the idea of it does get at the driving force of Chaos Theory. The common theme among the two is sensitive dependence on initial conditions. This is an idea that will be revisited later in the paper, because we must first cover the concepts necessary to frame chaos. This paper will explore one, two, and three dimensional systems, maps, bifurcations, limit cycles, attractors, and strange attractors before looking into the mechanics of chaos. Once chaos is introduced, we will look in depth at the Lorenz Equations. 2. One Dimensional Systems We begin our study by looking at nonlinear systems in one dimen- sion. One of the important features of these is the nonlinearity. Non- linearity in an equation evokes behavior that is not easily predicted due to the disproportionate nature of inputs and outputs. Also, the term “system”isoftenamisnomerbecauseitoftenevokestheideaof asystemofequations.Thiswillbethecaseaswemoveourfocuso↵ of one dimension, but for now we do not want to think of a system of equations. In this case, the type of system we want to consider is a first-order system of a single equation.
    [Show full text]
  • ONE HUNDRED YEARS of COMPLEX DYNAMICS the Subject of Complex Dynamics, That Is, the Behaviour of Orbits of Holomorphic Functions
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Liverpool Repository ONE HUNDRED YEARS OF COMPLEX DYNAMICS MARY REES The subject of Complex Dynamics, that is, the behaviour of orbits of holomorphic functions, emerged in the papers produced, independently, by Fatou and Julia, almost 100 years ago. Although the subject of Dynami- cal Systems did not then have a name, the dynamical properties found for holomorphic systems, even in these early researches, were so striking, so unusually comprehensive, and yet so varied, that these systems still attract widespread fascination, 100 years later. The first distinctive feature of iter- ation of a single holomorphic map f is the partition of either the complex plane or the Riemann sphere into two sets which are totally invariant under f: the Julia set | closed, nonempty, perfect, with dynamics which might loosely be called chaotic | and its complement | open, possibly empty, but, if non-empty, then with dynamics which were completely classified by the two pioneering researchers, modulo a few simply stated open questions. Before the subject re-emerged into prominence in the 1980's, the Julia set was alternately called the Fatou set, but Paul Blanchard introduced the idea of calling its complement the Fatou set, and this was immediately universally accepted. Probably the main reason for the remarkable rise in interest in complex dynamics, about thirty-five years ago, was the parallel with the subject of Kleinian groups, and hence with the whole subject of hyperbolic geome- try. A Kleinian group acting on the Riemann sphere is a dynamical system, with the sphere splitting into two disjoint invariant subsets, with the limit set and its complement, the domain of discontinuity, having exactly similar properties to the Julia and Fatou sets.
    [Show full text]
  • Maps, Chaos, and Fractals
    MATH305 Summer Research Project 2006-2007 Maps, Chaos, and Fractals Phillips Williams Department of Mathematics and Statistics University of Canterbury Maps, Chaos, and Fractals Phillipa Williams* MATH305 Mathematics Project University of Canterbury 9 February 2007 Abstract The behaviour and properties of one-dimensional discrete mappings are explored by writing Matlab code to iterate mappings and draw graphs. Fixed points, periodic orbits, and bifurcations are described and chaos is introduced using the logistic map. Symbolic dynamics are used to show that the doubling map and the logistic map have the properties of chaos. The significance of a period-3 orbit is examined and the concept of universality is introduced. Finally the Cantor Set provides a brief example of the use of iterative processes to generate fractals. *supervised by Dr. Alex James, University of Canterbury. 1 Introduction Devaney [1992] describes dynamical systems as "the branch of mathematics that attempts to describe processes in motion)) . Dynamical systems are mathematical models of systems that change with time and can be used to model either discrete or continuous processes. Contin­ uous dynamical systems e.g. mechanical systems, chemical kinetics, or electric circuits can be modeled by differential equations. Discrete dynamical systems are physical systems that involve discrete time intervals, e.g. certain types of population growth, daily fluctuations in the stock market, the spread of cases of infectious diseases, and loans (or deposits) where interest is compounded at fixed intervals. Discrete dynamical systems can be modeled by iterative maps. This project considers one-dimensional discrete dynamical systems. In the first section, the behaviour and properties of one-dimensional maps are examined using both analytical and graphical methods.
    [Show full text]
  • Analysis of the Coupled Logistic Map
    Theoretical Population Biology TP1365 Theoretical Population Biology 54, 1137 (1998) Article No. TP981365 Spatial Structure, Environmental Heterogeneity, and Population Dynamics: Analysis of the Coupled Logistic Map Bruce E. Kendall* Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, and National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, California 93106 and Gordon A. Fox- Department of Biology 0116, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0116, and Department of Biology, San Diego State University, San Diego, California 92182 Spatial extent can have two important consequences for population dynamics: It can generate spatial structure, in which individuals interact more intensely with neighbors than with more distant conspecifics, and it allows for environmental heterogeneity, in which habitat quality varies spatially. Studies of these features are difficult to interpret because the models are complex and sometimes idiosyncratic. Here we analyze one of the simplest possible spatial population models, to understand the mathematical basis for the observed patterns: two patches coupled by dispersal, with dynamics in each patch governed by the logistic map. With suitable choices of parameters, this model can represent spatial structure, environmental heterogeneity, or both in combination. We synthesize previous work and new analyses on this model, with two goals: to provide a comprehensive baseline to aid our understanding of more complex spatial models, and to generate predictions about the effects of spatial structure and environmental heterogeneity on population dynamics. Spatial structure alone can generate positive, negative, or zero spatial correlations between patches when dispersal rates are high, medium, or low relative to the complexity of the local dynamics.
    [Show full text]
  • The Logistic Map and Chaos
    The Logistic Map and Chaos Dylana Wilhelm James Madison University April 26, 2016 Dylana Wilhelm Logistic Map April 26, 2016 1 / 18 Logistic Map The logistic map is a first-order difference equation discovered to have complicated dynamics by mathematical biologist Robert May. The general form is given by xn+1 = rxn(1 − xn); where xn is the population of nth generation and r ≥ 0 is the growth rate. Dylana Wilhelm Logistic Map April 26, 2016 2 / 18 Applications of the Logistic Map Generally used in population biology to map the population at any time step to its values at the next time step Additional applications include: Genetics - change in gene frequency Epidemiology - fraction of population infected Economics - relationship between commodity quantity and price Social Sciences - number of people to have heard a rumor Dylana Wilhelm Logistic Map April 26, 2016 3 / 18 Logistic Map Derivation Derived from the logistic difference equation Nn+1 = Nn(r − aNn); by letting x = aN=r Results in simplest non-linear difference equation xn+1 = rxn(1 − xn) Parabola with a maximum value of r=4 at xn = 1=2 For 0 ≤ r ≤ 4, maps 0 ≤ xn ≤ 1 into itself Dylana Wilhelm Logistic Map April 26, 2016 4 / 18 Variation of Growth Rate Limit growth rate to interval 0 ≤ r ≤ 4 Range of behavior as r is varied: Population reaches extinction for r < 1 Non-trivial steady state for 1 < r < 3 Fluctuations in population for r > 3 Dylana Wilhelm Logistic Map April 26, 2016 5 / 18 Variation of Growth Rate Dylana Wilhelm Logistic Map April 26, 2016 6 / 18 Fixed Points of Logistic Map Fixed points satisfy the equation f (x∗) = x∗ = rx∗(1 − x∗); which gives x∗(1 − r + rx∗) = 0: Thus, we have that 1 x∗ = 0 or x∗ = 1 − : r Dylana Wilhelm Logistic Map April 26, 2016 7 / 18 Stability of Fixed Points Stability is given by jf 0(x∗)j < 1.
    [Show full text]
  • Stability and Fractal Patterns of Complex Logistic Map
    BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES • Volume 14, No 3 Sofia • 2014 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.2478/cait-2014-0029 Stability and Fractal Patterns of Complex Logistic Map Bhagwati Prasad, Kuldip Katiyar Department of Mathematics, Jaypee Institute of Information Technology, A-10, Sector-62, Noida, UP-201307 INDIA Emails: [email protected], [email protected] Abstract: The intent of this paper is to study the fractal patterns of one dimensional complex logistic map by finding the optimum values of the control parameter using Ishikawa iterative scheme. The logistic map is shown to have bounded and stable behaviour for larger values of the control parameter. This is well depicted via time series analysis and interesting fractal patterns as well are presented. Keywords: Complex logistic map, Ishikawa iteration, fractals, Julia set. 1. Introduction The name “logistic growth model” is essentially due to Verhulst [20] which he used for the studies on population dynamics. He introduced logistic equation for demographic modelling by extending the Malthus equation of the continuous growth of a population with a view to obtain a stable stationary finite state (see [4]). It took more than hundred years to recognize his founding contributions towards the population dynamics and non-linear sciences. This work received wide attention due to the great implications of the simple looking equation in Chaos theory. In 1963, Edward Lorenz introduced an equivalent version of the logistic model for his famous weather forecast model [13]. R. M a y [15, 16], in 1976, recognized the importance of the logistic model and observed that the continuous time model may not be suitable to reflect the realities in most of the cases and constructed a discrete 14 version of this model.
    [Show full text]
  • Pseudo-Random Number Generator Based on Logistic Chaotic System
    entropy Article Pseudo-Random Number Generator Based on Logistic Chaotic System Luyao Wang and Hai Cheng * Electronic Engineering College, Heilongjiang University, Harbin 150080, China; [email protected] * Correspondence: [email protected] Received: 23 August 2019; Accepted: 27 September 2019; Published: 30 September 2019 Abstract: In recent years, a chaotic system is considered as an important pseudo-random source to pseudo-random number generators (PRNGs). This paper proposes a PRNG based on a modified logistic chaotic system. This chaotic system with fixed system parameters is convergent and its chaotic behavior is analyzed and proved. In order to improve the complexity and randomness of modified PRNGs, the chaotic system parameter denoted by floating point numbers generated by the chaotic system is confused and rearranged to increase its key space and reduce the possibility of an exhaustive attack. It is hard to speculate on the pseudo-random number by chaotic behavior because there is no statistical characteristics and infer the pseudo-random number generated by chaotic behavior. The system parameters of the next chaotic system are related to the chaotic values generated by the previous ones, which makes the PRNG generate enough results. By confusing and rearranging the output sequence, the system parameters of the previous time cannot be gotten from the next time which ensures the security. The analysis shows that the pseudo-random sequence generated by this method has perfect randomness, cryptographic properties and can pass the statistical tests. Keywords: logistic chaotic system; PRNG; Pseudo-random number sequence 1. Introduction With the rapid development of communication technology and the wide use of the Internet and mobile networks, people pay more and more attention to information security.
    [Show full text]
  • Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction
    systems Communication Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction Geoff Boeing Department of City and Regional Planning, University of California, Berkeley, CA 94720, USA; [email protected]; Tel.: +1-510-642-6000 Academic Editor: Ockie Bosch Received: 7 September 2016; Accepted: 7 November 2016; Published: 13 November 2016 Abstract: Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems’ behavior. Keywords: visualization; nonlinear dynamics; chaos; fractal; attractor; bifurcation; dynamical systems; prediction; python; logistic map 1. Introduction Chaos theory is a branch of mathematics that deals with nonlinear dynamical systems.
    [Show full text]