Analysis of the Coupled Logistic Map
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A General Representation of Dynamical Systems for Reservoir Computing
A general representation of dynamical systems for reservoir computing Sidney Pontes-Filho∗,y,x, Anis Yazidi∗, Jianhua Zhang∗, Hugo Hammer∗, Gustavo B. M. Mello∗, Ioanna Sandvigz, Gunnar Tuftey and Stefano Nichele∗ ∗Department of Computer Science, Oslo Metropolitan University, Oslo, Norway yDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway zDepartment of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway Email: [email protected] Abstract—Dynamical systems are capable of performing com- TABLE I putation in a reservoir computing paradigm. This paper presents EXAMPLES OF DYNAMICAL SYSTEMS. a general representation of these systems as an artificial neural network (ANN). Initially, we implement the simplest dynamical Dynamical system State Time Connectivity system, a cellular automaton. The mathematical fundamentals be- Cellular automata Discrete Discrete Regular hind an ANN are maintained, but the weights of the connections Coupled map lattice Continuous Discrete Regular and the activation function are adjusted to work as an update Random Boolean network Discrete Discrete Random rule in the context of cellular automata. The advantages of such Echo state network Continuous Discrete Random implementation are its usage on specialized and optimized deep Liquid state machine Discrete Continuous Random learning libraries, the capabilities to generalize it to other types of networks and the possibility to evolve cellular automata and other dynamical systems in terms of connectivity, update and learning rules. Our implementation of cellular automata constitutes an reservoirs [6], [7]. Other systems can also exhibit the same initial step towards a general framework for dynamical systems. dynamics. The coupled map lattice [8] is very similar to It aims to evolve such systems to optimize their usage in reservoir CA, the only exception is that the coupled map lattice has computing and to model physical computing substrates. -
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. -
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. -
Thermodynamic Properties of Coupled Map Lattices 1 Introduction
Thermodynamic properties of coupled map lattices J´erˆome Losson and Michael C. Mackey Abstract This chapter presents an overview of the literature which deals with appli- cations of models framed as coupled map lattices (CML’s), and some recent results on the spectral properties of the transfer operators induced by various deterministic and stochastic CML’s. These operators (one of which is the well- known Perron-Frobenius operator) govern the temporal evolution of ensemble statistics. As such, they lie at the heart of any thermodynamic description of CML’s, and they provide some interesting insight into the origins of nontrivial collective behavior in these models. 1 Introduction This chapter describes the statistical properties of networks of chaotic, interacting el- ements, whose evolution in time is discrete. Such systems can be profitably modeled by networks of coupled iterative maps, usually referred to as coupled map lattices (CML’s for short). The description of CML’s has been the subject of intense scrutiny in the past decade, and most (though by no means all) investigations have been pri- marily numerical rather than analytical. Investigators have often been concerned with the statistical properties of CML’s, because a deterministic description of the motion of all the individual elements of the lattice is either out of reach or uninteresting, un- less the behavior can somehow be described with a few degrees of freedom. However there is still no consistent framework, analogous to equilibrium statistical mechanics, within which one can describe the probabilistic properties of CML’s possessing a large but finite number of elements. -
A Simple Scalar Coupled Map Lattice Model for Excitable Media
This is a repository copy of A simple scalar coupled map lattice model for excitable media. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/74667/ Monograph: Guo, Y., Zhao, Y., Coca, D. et al. (1 more author) (2010) A simple scalar coupled map lattice model for excitable media. Research Report. ACSE Research Report no. 1016 . Automatic Control and Systems Engineering, University of Sheffield Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ A Simple Scalar Coupled Map Lattice Model for Excitable Media Yuzhu Guo, Yifan Zhao, Daniel Coca, and S. A. Billings Research Report No. 1016 Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street, Sheffield, S1 3JD, UK 8 September 2010 A Simple Scalar Coupled Map Lattice Model for Excitable Media Yuzhu Guo, Yifan Zhao, Daniel Coca, and S.A. -
Coupled Map Lattice (CML) Approach
Different mechanisms of synchronization : Coupled Map Lattice (CML) approach – p.1 Outline Synchronization : an universal phenomena Model (Coupled maps/oscillators on Networks) – p.2 Modelling Spatio-temporal dynamics 2- and 3-dimensional fluid flow, Coupled-oscillator models, Cellular automata, Transport models, Coupled map models Model does not have direct connections with actual physical or biological systems – p.3 Modelling Spatio-temporal dynamics 2- and 3-dimensional fluid flow, Coupled-oscillator models, Cellular automata, Transport models, Coupled map models Model does not have direct connections with actual physical or biological systems General results show some universal features – p.3 We select coupled maps(chaotic), oscillators on Networks Models. – p.4 Coupled Dynamics on Networks ε xi(t +1) = f(xi(t)) + Cijg(xi(t),xj(t)) Cij Pj P Periodic orbit Synchronized chaos Travelling wave Spatio-temporal chaos – p.5 Coupled maps Models Coupled oscillators (- Kuramoto ∼ beginning of 1980’s) Coupled maps (- K. Kaneko) - K. Kaneko, ”Simulating Physics with Coupled Map Lattices —— Pattern Dynamics, Information Flow, and Thermodynamics of Spatiotemporal Chaos”, pp1-52 in Formation, Dynamics, and Statistics of Patterns ed. K. Kawasaki et.al., World. Sci. 1990” – p.6 Studying real systems with coupled maps/oscillators models: - Ott, Kurths, Strogatz, Geisel, Mikhailov, Wolf etc . From Kuramoto to Crawford .... (Physica D, Sep 2000) Chaotic Transients in Complex Networks, (Phys Rev Lett, 2004) Turbulence in oscillatory chemical reactions, (J Chem Phys, 1994) Crowd synchrony on the Millennium Bridge, (Nature, 2005) Species extinction - Amritkar and Rangarajan, Phys. Rev. Lett. (2006) – p.7 SYNCHRONIZATION First observation (Pendulum clock (1665)) - discovered in 1665 by the famous Dutch physicists, Christiaan Huygenes. -
Predictability: a Way to Characterize Complexity
Predictability: a way to characterize Complexity G.Boffetta (a), M. Cencini (b,c), M. Falcioni(c), and A. Vulpiani (c) (a) Dipartimento di Fisica Generale, Universit`adi Torino, Via Pietro Giuria 1, I-10125 Torino, Italy and Istituto Nazionale Fisica della Materia, Unit`adell’Universit`adi Torino (b) Max-Planck-Institut f¨ur Physik komplexer Systeme, N¨othnitzer Str. 38 , 01187 Dresden, Germany (c) Dipartimento di Fisica, Universit`adi Roma ”la Sapienza”, Piazzale Aldo Moro 5, 00185 Roma, Italy and Istituto Nazionale Fisica della Materia, Unit`adi Roma 1 Contents 1 Introduction 4 2 Two points of view 6 2.1 Dynamical systems approach 6 2.2 Information theory approach 11 2.3 Algorithmic complexity and Lyapunov Exponent 18 3 Limits of the Lyapunov exponent for predictability 21 3.1 Characterization of finite-time fluctuations 21 3.2 Renyi entropies 25 3.3 The effects of intermittency on predictability 26 3.4 Growth of non infinitesimal perturbations 28 arXiv:nlin/0101029v1 [nlin.CD] 17 Jan 2001 3.5 The ǫ-entropy 32 4 Predictability in extended systems 34 4.1 Simplified models for extended systems and the thermodynamic limit 35 4.2 Overview on the predictability problem in extended systems 37 4.3 Butterfly effect in coupled map lattices 39 4.4 Comoving and Specific Lyapunov Exponents 41 4.5 Convective chaos and spatial complexity 42 4.6 Space-time evolution of localized perturbations 46 4.7 Macroscopic chaos in Globally Coupled Maps 49 Preprint submitted to Elsevier Preprint 30 April 2018 4.8 Predictability in presence of coherent structures 51 5 -
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. -
A Robust Image-Based Cryptology Scheme Based on Cellular Non-Linear Network and Local Image Descriptors
PRE-PRINT IJPEDS A robust image-based cryptology scheme based on cellular non-linear network and local image descriptors Mohammad Mahdi Dehshibia, Jamshid Shanbehzadehb, and Mir Mohsen Pedramb aDepartment of Computer Engineering, Faculty of Computer and Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran; bDepartment of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran ARTICLE HISTORY Compiled August 14, 2018 ABSTRACT Cellular nonlinear network (CNN) provides an infrastructure for Cellular Automata to have not only an initial state but an input which has a local memory in each cell with much more complexity. This property has many applications which we have investigated it in proposing a robust cryptology scheme. This scheme consists of a cryptography and steganography sub-module in which a 3D CNN is designed to produce a chaotic map as the kernel of the system to preserve confidentiality and data integrity in cryptology. Our contributions are three-fold including (1) a feature descriptor is applied to the cover image to form the secret key while conventional methods use a predefined key, (2) a 3D CNN is used to make a chaotic map for making cipher from the visual message, and (3) the proposed CNN is also used to make a dynamic k-LSB steganography. Conducted experiments on 25 standard images prove the effectiveness of the proposed cryptology scheme in terms of security, visual, and complexity analysis. KEYWORDS Cryptography; Steganography; Chaotic map; Cellular Non-linear Network; Image Descriptor; Complexity 1. Introduction Sensitive and personal data are parts of data transmission over the Internet which have always been suspicious to be intercepted. -
SPATIOTEMPORAL CHAOS in COUPLED MAP LATTICE Itishree
SPATIOTEMPORAL CHAOS IN COUPLED MAP LATTICE By Itishree Priyadarshini Under the Guidance of Prof. Biplab Ganguli Department of Physics National Institute of Technology, Rourkela CERTIFICATE This is to certify that the project thesis entitled ” Spatiotemporal chaos in Cou- pled Map Lattice ” being submitted by Itishree Priyadarshini in partial fulfilment to the requirement of the one year project course (PH 592) of MSc Degree in physics of National Institute of Technology, Rourkela has been carried out under my super- vision. The result incorporated in the thesis has been produced by developing her own computer codes. Prof. Biplab Ganguli Dept. of Physics National Institute of Technology Rourkela - 769008 1 ACKNOWLEDGEMENT I would like to acknowledge my guide Prof. Biplab Ganguli for his help and guidance in the completion of my one-year project and also for his enormous moti- vation and encouragement. I am also very much thankful to research scholars whose encouragement and support helped me to complete my project. 2 ABSTRACT The sensitive dependence on initial condition, which is the essential feature of chaos is demonstrated through simple Lorenz model. Period doubling route to chaos is shown by analysis of Logistic map and other different route to chaos is discussed. Coupled map lattices are investigated as a model for spatio-temporal chaos. Diffusively coupled logistic lattice is studied which shows different pattern in accordance with the coupling constant and the non-linear parameter i.e. frozen random pattern, pattern selection with suppression of chaos , Brownian motion of the space defect, intermittent collapse, soliton turbulence and travelling waves. 3 Contents 1 Introduction 3 2 Chaos 3 3 Lorenz System 4 4 Route to Chaos 6 4.1 PeriodDoubling............................. -
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. -
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.