Iterated Function System Iterated Function System

Total Page:16

File Type:pdf, Size:1020Kb

Iterated Function System Iterated Function System Iterated Function System Iterated Function System (or I.F.S. for short) is a program that explores the various aspects of the Iterated Function System. It contains three modes: 1. The Chaos Game, which implements variations of the chaos game popularized by Michael Barnsley, 2. Deterministic Mode, which creates fractals using iterated function system rules in a deterministic way, and 3. Random Mode, which creates fractals using iterated function system rules in a random way. Upon execution of I.F.S. we need to choose a mode. Let’s begin by choosing the Chaos Game. Upon selecting that mode, two windows will appear as in Figure 1. The blank one on the right is the graph window and is where the result of the execution of the chaos game will be displayed. The window on the left is the status window. It is set up to play the chaos game using three corners and a scale factor of 0.5. Thus, the next seed point will be calculated as the midpoint of the current seed point with the selected corner. It is also set up so that the corners will be graphed once they are selected, and a line is not drawn between the current seed point and the next selected corner. As a first attempt at playing the chaos game, let’s press the “Go!” button without changing any of these settings. We will first be asked to define corner #1, which can be accomplished either by typing in the xy-coordinates in the status window edit boxes, or by using the mouse to move the cross hair cursor appearing in the graph window. To activate the second option, we just drag the mouse and position the cross hair cursor where we want it. We choose a color for the corner before pressing the “OK” button. We repeat the process for the other corner points. Next we will be asked to define the initial seed point. Like the corner points, we can either type the coordinates in the status window edit boxes, or we can use the mouse. Upon clicking the “OK” button, we will be given the option of whether or not to plot the seed point. Next we will be asked to choose a corner (or let Chaos Game choose one at random) with the option of whether or not to plot the next seed point. After that we will be given three options: 1. Repeat the process of manually choosing a corner by hand, 2. Automate the process, or 3. Stop When we select the “Automate” option, the new seed points are plotted in rapid succession and in the colors determined by the corner points from which they were defined. The calculations and point plotting will continue until the “Pause” button is pushed at which point we will be given the option to continue or stop. Figure 1 shows the result after a while when Corner #1 was defined as having coordinates (0, 1) and associated color red, Corner #2 was defined as having coordinates(0, 0) and associated color green, and Corner #3 was defined as having coordinates (1, 0) and associated color blue. Figure 1 Figure 2 shows the result of running the chaos game with five corners and scale factor 0.5. Note: The pentagon formed is composed of five (overlapping) smaller pentagons, There is a hole in the middle of the pentagon, which is not covered by any of the smaller pentagons. Except for the color, each of the smaller pentagons is a scaled down version of the larger one. Figure 2 Next, let’s switch over to the deterministic mode. We have the choice of creating one of the pre- defined fractals or we can define our own iterated function system rules to define our own fractal. For starters, let’s press the “Go” button while “Left Gasket” is selected. A three by three grid of squares appears in the graph window and within the upper left hand square, a unit square ([0, 1] × [0, 1]) with a “V” inside appears. At the first iteration, the iterated function system will apply three rules, each one creating a scaled down copy of the unit square with a “V”. The rules are: 1. Multiple each x-coordinate and each y-coordinate by the scale factor 0.5. 2. Multiple each x-coordinate and each y-coordinate by the scale factor 0.5, then add 0.5 to each x-coordinate. 3. Multiple each x-coordinate and each y-coordinate by the scale factor 0.5, then add 0.5 to each y-coordinate. The three resulting squares will appear in the upper center grid square when the “Next” button is pressed. At the second iteration these same rules are applied to each of the squares created in the first iteration, resulting in nine squares. More generally, at the nth iteration, the three rules are applied to the 3n1 squares created at the previous iteration, thus creating 3n new squares. Figure 3 shows the result of pressing the “Next” button a total of eight times. Figure 3 Now let’s try to define our own iterated function rules to create our own fractal. To do this, we first choose the “Home Made” option. We will first be asked to determine the number of rules we will be using. For the purposes of this example let’s choose three rules, then press the “OK” button. The screen should look like Figure 4. For each rule there are associated six parameters: a scale factor in the x-direction, a scale factor in the y-direction, a rotation factor in the x- direction, a rotation factor in the y-direction, a translation factor in the x-direction, and a translation factor in the y-direction. The parameters that appear are those for the most recently created fractal, or if no fractal has been created yet, the left gasket. For rule #1 we define the two scale factors as 0.5, and the rotation and translation factors as 0. For rule #2 we define the scale factors as 0.5, the two rotation factors as -90 (degrees) and the y-direction translation factor as 1, leaving the x-direction translation factor at 0. For rule #3 we define the scale factors as 0.5, the rotation factors as 180, and both translation factors as 1. After we have defined all the parameters, we press the “OK” button at the bottom of the status window. Figure 4 Once the “Next” button is pressed, our screen will look like Figure 5. Note that in the upper center block three smaller squares have been formed. The lower left square, created from rule #2, is simply the original unit square where every x- and y-coordinate has been multiplied by 0.5. The upper left square, created from rule #2, is the original unit square where every x- and y- coordinate has been multiplied by 0.5, then the entire square was rotated clockwise 90 degrees (or counterclockwise -90 degrees), and finally the scaled, rotated square was translated up 1 unit. The vertex of the “V” is located on the left side of the square due to the rotation. The upper right square, created from rule #3, is the original unit square where every x- and y-coordinate has been multiplied by 0.5, then the entire square was rotated counterclockwise 180 degrees, and finally the scaled, rotated square was translated up one unit and to the right one unit. The vertex of the “V” is located on the top edge due to the rotation. Figure 6 shows the step-by-step creation of the resulting fractal after 8 steps. Figure 5 The third mode is “Random”. It starts with a predetermined “seed” point, randomly selects one of the defined iterated function system rules, and applies that rule to the seed to create and plot a new seed. This process continues until the user presses the pause button. Figure 7 shows the result of choosing “Equilateral Gasket” and letting the program run for several seconds before pressing the “Pause” button. Now let’s try to define our own iterated function rules to create our own fractal. To do this, we first choose the “Home Made” option. We will first be asked to determine the number of rules we will be using. For the purposes of this example let’s choose three rules, then press the “OK” button. The window on the left should look like Figure 8. We can determine the dimensions for the window and the coordinates for the initial point of the sequence to be generated (the seed). In addition, for each rule there are associated seven parameters: a scale factor in the x-direction, a scale factor in the y-direction, a rotation factor in the x-direction, a rotation factor in the y- direction, a translation factor in the x-direction, a translation factor in the y-direction, and the Figure 6 probability that the rule will be chosen. The parameters that appear are those for the most recently created fractal, or if no fractal has been created yet, the left gasket. For rule #1 let’s define the two scale factors as 0.5, and the rotation and translation factors as 0. For rule #2 we define the scale factors as 0.5, the two rotation factors as 0 and the x-direction translation factor as 0.5, leaving the y-direction translation factor at 0. For rule #3 we define the scale factors as 0.5, the rotation factors as 90, the x-direction translation factor as 1, and the y-direction 1 translation factor as 0.5.
Recommended publications
  • Iterated Function System Quasiarcs
    CONFORMAL GEOMETRY AND DYNAMICS An Electronic Journal of the American Mathematical Society Volume 21, Pages 78–100 (February 3, 2017) http://dx.doi.org/10.1090/ecgd/305 ITERATED FUNCTION SYSTEM QUASIARCS ANNINA ISELI AND KEVIN WILDRICK Abstract. We consider a class of iterated function systems (IFSs) of contract- ing similarities of Rn, introduced by Hutchinson, for which the invariant set possesses a natural H¨older continuous parameterization by the unit interval. When such an invariant set is homeomorphic to an interval, we give necessary conditions in terms of the similarities alone for it to possess a quasisymmetric (and as a corollary, bi-H¨older) parameterization. We also give a related nec- essary condition for the invariant set of such an IFS to be homeomorphic to an interval. 1. Introduction Consider an iterated function system (IFS) of contracting similarities S = n {S1,...,SN } of R , N ≥ 2, n ≥ 1. For i =1,...,N, we will denote the scal- n n ing ratio of Si : R → R by 0 <ri < 1. In a brief remark in his influential work [18], Hutchinson introduced a class of such IFSs for which the invariant set is a Peano continuum, i.e., it possesses a continuous parameterization by the unit interval. There is a natural choice for this parameterization, which we call the Hutchinson parameterization. Definition 1.1 (Hutchinson, 1981). The pair (S,γ), where γ is the invariant set of an IFS S = {S1,...,SN } with scaling ratio list {r1,...,rN } is said to be an IFS path, if there exist points a, b ∈ Rn such that (i) S1(a)=a and SN (b)=b, (ii) Si(b)=Si+1(a), for any i ∈{1,...,N − 1}.
    [Show full text]
  • An Introduction to Apophysis © Clive Haynes MMXX
    Apophysis Fractal Generator An Introduction Clive Haynes Fractal ‘Flames’ The type of fractals generated are known as ‘Flame Fractals’ and for the curious, I append a note about their structure, gleaned from the internet, at the end of this piece. Please don’t ask me to explain it! Where to download Apophysis: go to https://sourceforge.net/projects/apophysis/ Sorry Mac users but it’s only available for Windows. To see examples of fractal images I’ve generated using Apophysis, I’ve made an Issuu e-book and here’s the URL. https://issuu.com/fotopix/docs/ordering_kaos Getting Started There’s not a defined ‘follow this method workflow’ for generating interesting fractals. It’s really a matter of considerable experimentation and the accumulation of a knowledge-base about general principles: what the numerous presets tend to do and what various options allow. Infinite combinations of variables ensure there’s also a huge serendipity factor. I’ve included a few screen-grabs to help you. The screen-grabs are detailed and you may need to enlarge them for better viewing. Once Apophysis has loaded, it will provide a Random Batch of fractal patterns. Some will be appealing whilst many others will be less favourable. To generate another set, go to File > Random Batch (shortcut Ctrl+B). Screen-grab 1 Choose a fractal pattern from the batch and it will appear in the main window (Screen-grab 1). Depending upon the complexity of the fractal and the processing power of your computer, there will be a ‘wait time’ every time you change a parameter.
    [Show full text]
  • Homeomorphisms Group of Normed Vector Space: Conjugacy Problems
    HOMEOMORPHISMS GROUP OF NORMED VECTOR SPACE: CONJUGACY PROBLEMS AND THE KOOPMAN OPERATOR MICKAEL¨ D. CHEKROUN AND JEAN ROUX Abstract. This article is concerned with conjugacy problems arising in the homeomorphisms group, Hom(F ), of unbounded subsets F of normed vector spaces E. Given two homeomor- phisms f and g in Hom(F ), it is shown how the existence of a conjugacy may be related to the existence of a common generalized eigenfunction of the associated Koopman operators. This common eigenfunction serves to build a topology on Hom(F ), where the conjugacy is obtained as limit of a sequence generated by the conjugacy operator, when this limit exists. The main conjugacy theorem is presented in a class of generalized Lipeomorphisms. 1. Introduction In this article we consider the conjugacy problem in the homeomorphisms group of a finite dimensional normed vector space E. It is out of the scope of the present work to review the problem of conjugacy in general, and the reader may consult for instance [13, 16, 29, 33, 26, 42, 45, 51, 52] and references therein, to get a partial survey of the question from a dynamical point of view. The present work raises the problem of conjugacy in the group Hom(F ) consisting of homeomorphisms of an unbounded subset F of E and is intended to demonstrate how the conjugacy problem, in such a case, may be related to spectral properties of the associated Koopman operators. In this sense, this paper provides new insights on the relations between the spectral theory of dynamical systems [5, 17, 23, 36] and the topological conjugacy problem [51, 52]1.
    [Show full text]
  • Iterated Function Systems, Ruelle Operators, and Invariant Projective Measures
    MATHEMATICS OF COMPUTATION Volume 75, Number 256, October 2006, Pages 1931–1970 S 0025-5718(06)01861-8 Article electronically published on May 31, 2006 ITERATED FUNCTION SYSTEMS, RUELLE OPERATORS, AND INVARIANT PROJECTIVE MEASURES DORIN ERVIN DUTKAY AND PALLE E. T. JORGENSEN Abstract. We introduce a Fourier-based harmonic analysis for a class of dis- crete dynamical systems which arise from Iterated Function Systems. Our starting point is the following pair of special features of these systems. (1) We assume that a measurable space X comes with a finite-to-one endomorphism r : X → X which is onto but not one-to-one. (2) In the case of affine Iterated Function Systems (IFSs) in Rd, this harmonic analysis arises naturally as a spectral duality defined from a given pair of finite subsets B,L in Rd of the same cardinality which generate complex Hadamard matrices. Our harmonic analysis for these iterated function systems (IFS) (X, µ)is based on a Markov process on certain paths. The probabilities are determined by a weight function W on X. From W we define a transition operator RW acting on functions on X, and a corresponding class H of continuous RW - harmonic functions. The properties of the functions in H are analyzed, and they determine the spectral theory of L2(µ).ForaffineIFSsweestablish orthogonal bases in L2(µ). These bases are generated by paths with infinite repetition of finite words. We use this in the last section to analyze tiles in Rd. 1. Introduction One of the reasons wavelets have found so many uses and applications is that they are especially attractive from the computational point of view.
    [Show full text]
  • Recursion, Writing, Iteration. a Proposal for a Graphics Foundation of Computational Reason Luca M
    Recursion, Writing, Iteration. A Proposal for a Graphics Foundation of Computational Reason Luca M. Possati To cite this version: Luca M. Possati. Recursion, Writing, Iteration. A Proposal for a Graphics Foundation of Computa- tional Reason. 2015. hal-01321076 HAL Id: hal-01321076 https://hal.archives-ouvertes.fr/hal-01321076 Preprint submitted on 24 May 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1/16 Recursion, Writing, Iteration A Proposal for a Graphics Foundation of Computational Reason Luca M. Possati In this paper we present a set of philosophical analyses to defend the thesis that computational reason is founded in writing; only what can be written is computable. We will focus on the relations among three main concepts: recursion, writing and iteration. The most important questions we will address are: • What does it mean to compute something? • What is a recursive structure? • Can we clarify the nature of recursion by investigating writing? • What kind of identity is presupposed by a recursive structure and by computation? Our theoretical path will lead us to a radical revision of the philosophical notion of identity. The act of iterating is rooted in an abstract space – we will try to outline a topological description of iteration.
    [Show full text]
  • Bézier Curves Are Attractors of Iterated Function Systems
    New York Journal of Mathematics New York J. Math. 13 (2007) 107–115. All B´ezier curves are attractors of iterated function systems Chand T. John Abstract. The fields of computer aided geometric design and fractal geom- etry have evolved independently of each other over the past several decades. However, the existence of so-called smooth fractals, i.e., smooth curves or sur- faces that have a self-similar nature, is now well-known. Here we describe the self-affine nature of quadratic B´ezier curves in detail and discuss how these self-affine properties can be extended to other types of polynomial and ra- tional curves. We also show how these properties can be used to control shape changes in complex fractal shapes by performing simple perturbations to smooth curves. Contents 1. Introduction 107 2. Quadratic B´ezier curves 108 3. Iterated function systems 109 4. An IFS with a QBC attractor 110 5. All QBCs are attractors of IFSs 111 6. Controlling fractals with B´ezier curves 112 7. Conclusion and future work 114 References 114 1. Introduction In the late 1950s, advancements in hardware technology made it possible to effi- ciently manufacture curved 3D shapes out of blocks of wood or steel. It soon became apparent that the bottleneck in mass production of curved 3D shapes was the lack of adequate software for designing these shapes. B´ezier curves were first introduced in the 1960s independently by two engineers in separate French automotive compa- nies: first by Paul de Casteljau at Citro¨en, and then by Pierre B´ezier at R´enault.
    [Show full text]
  • Summary of Unit 1: Iterated Functions 1
    Summary of Unit 1: Iterated Functions 1 Summary of Unit 1: Iterated Functions David P. Feldman http://www.complexityexplorer.org/ Summary of Unit 1: Iterated Functions 2 Functions • A function is a rule that takes a number as input and outputs another number. • A function is an action. • Functions are deterministic. The output is determined only by the input. x f(x) f David P. Feldman http://www.complexityexplorer.org/ Summary of Unit 1: Iterated Functions 3 Iteration and Dynamical Systems • We iterate a function by turning it into a feedback loop. • The output of one step is used as the input for the next. • An iterated function is a dynamical system, a system that evolves in time according to a well-defined, unchanging rule. x f(x) f David P. Feldman http://www.complexityexplorer.org/ Summary of Unit 1: Iterated Functions 4 Itineraries and Seeds • We iterate a function by applying it again and again to a number. • The number we start with is called the seed or initial condition and is usually denoted x0. • The resulting sequence of numbers is called the itinerary or orbit. • It is also sometimes called a time series or a trajectory. • The iterates are denoted xt. Ex: x5 is the fifth iterate. David P. Feldman http://www.complexityexplorer.org/ Summary of Unit 1: Iterated Functions 5 Time Series Plots • A useful way to visualize an itinerary is with a time series plot. 0.8 0.7 0.6 0.5 t 0.4 x 0.3 0.2 0.1 0.0 0 1 2 3 4 5 6 7 8 9 10 time t • The time series plotted above is: 0.123, 0.189, 0.268, 0.343, 0.395, 0.418, 0.428, 0.426, 0.428, 0.429, 0.429.
    [Show full text]
  • Fractal Dimension of Self-Affine Sets: Some Examples
    FRACTAL DIMENSION OF SELF-AFFINE SETS: SOME EXAMPLES G. A. EDGAR One of the most common mathematical ways to construct a fractal is as a \self-similar" set. A similarity in Rd is a function f : Rd ! Rd satisfying kf(x) − f(y)k = r kx − yk for some constant r. We call r the ratio of the map f. If f1; f2; ··· ; fn is a finite list of similarities, then the invariant set or attractor of the iterated function system is the compact nonempty set K satisfying K = f1[K] [ f2[K] [···[ fn[K]: The set K obtained in this way is said to be self-similar. If fi has ratio ri < 1, then there is a unique attractor K. The similarity dimension of the attractor K is the solution s of the equation n X s (1) ri = 1: i=1 This theory is due to Hausdorff [13], Moran [16], and Hutchinson [14]. The similarity dimension defined by (1) is the Hausdorff dimension of K, provided there is not \too much" overlap, as specified by Moran's open set condition. See [14], [6], [10]. REPRINT From: Measure Theory, Oberwolfach 1990, in Supplemento ai Rendiconti del Circolo Matematico di Palermo, Serie II, numero 28, anno 1992, pp. 341{358. This research was supported in part by National Science Foundation grant DMS 87-01120. Typeset by AMS-TEX. G. A. EDGAR I will be interested here in a generalization of self-similar sets, called self-affine sets. In particular, I will be interested in the computation of the Hausdorff dimension of such sets.
    [Show full text]
  • On Bounding Boxes of Iterated Function System Attractors Hsueh-Ting Chu, Chaur-Chin Chen*
    Computers & Graphics 27 (2003) 407–414 Technical section On bounding boxes of iterated function system attractors Hsueh-Ting Chu, Chaur-Chin Chen* Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan, ROC Abstract Before rendering 2D or 3D fractals with iterated function systems, it is necessary to calculate the bounding extent of fractals. We develop a new algorithm to compute the bounding boxwhich closely contains the entire attractor of an iterated function system. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Fractals; Iterated function system; IFS; Bounding box 1. Introduction 1.1. Iterated function systems Barnsley [1] uses iterated function systems (IFS) to Definition 1. A transform f : X-X on a metric space provide a framework for the generation of fractals. ðX; dÞ is called a contractive mapping if there is a Fractals are seen as the attractors of iterated function constant 0pso1 such that systems. Based on the framework, there are many A algorithms to generate fractal pictures [1–4]. However, dðf ðxÞ; f ðyÞÞps Á dðx; yÞ8x; y X; ð1Þ in order to generate fractals, all of these algorithms have where s is called a contractivity factor for f : to estimate the bounding boxes of fractals in advance. For instance, in the program Fractint (http://spanky. Definition 2. In a complete metric space ðX; dÞ; an triumf.ca/www/fractint/fractint.html), we have to guess iterated function system (IFS) [1] consists of a finite set the parameters of ‘‘image corners’’ before the beginning of contractive mappings w ; for i ¼ 1; 2; y; n; which is of drawing, which may not be practical.
    [Show full text]
  • Solving Iterated Functions Using Genetic Programming Michael D
    Solving Iterated Functions Using Genetic Programming Michael D. Schmidt Hod Lipson Computational Synthesis Lab Computational Synthesis Lab Cornell University Cornell University Ithaca, NY 14853 Ithaca, NY 14853 [email protected] [email protected] ABSTRACT various communities. Renowned physicist Michael Fisher is An iterated function f(x) is a function that when composed with rumored to have solved the puzzle within five minutes [2]; itself, produces a given expression f(f(x))=g(x). Iterated functions however, few have matched this feat. are essential constructs in fractal theory and dynamical systems, The problem is enticing because of its apparent simplicity. Similar but few analysis techniques exist for solving them analytically. problems such as f(f(x)) = x2, or f(f(x)) = x4 + b are straightforward Here we propose using genetic programming to find analytical (see Table 1). The fact that the slight modification from these solutions to iterated functions of arbitrary form. We demonstrate easier functions makes the problem much more challenging this technique on the notoriously hard iterated function problem of highlights the difficulty in solving iterated function problems. finding f(x) such that f(f(x))=x2–2. While some analytical techniques have been developed to find a specific solution to Table 1. A few example iterated functions problems. problems of this form, we show that it can be readily solved using genetic programming without recourse to deep mathematical Iterated Function Solution insight. We find a previously unknown solution to this problem, suggesting that genetic programming may be an essential tool for f(f(x)) = x f(x) = x finding solutions to arbitrary iterated functions.
    [Show full text]
  • Math Morphing Proximate and Evolutionary Mechanisms
    Curriculum Units by Fellows of the Yale-New Haven Teachers Institute 2009 Volume V: Evolutionary Medicine Math Morphing Proximate and Evolutionary Mechanisms Curriculum Unit 09.05.09 by Kenneth William Spinka Introduction Background Essential Questions Lesson Plans Website Student Resources Glossary Of Terms Bibliography Appendix Introduction An important theoretical development was Nikolaas Tinbergen's distinction made originally in ethology between evolutionary and proximate mechanisms; Randolph M. Nesse and George C. Williams summarize its relevance to medicine: All biological traits need two kinds of explanation: proximate and evolutionary. The proximate explanation for a disease describes what is wrong in the bodily mechanism of individuals affected Curriculum Unit 09.05.09 1 of 27 by it. An evolutionary explanation is completely different. Instead of explaining why people are different, it explains why we are all the same in ways that leave us vulnerable to disease. Why do we all have wisdom teeth, an appendix, and cells that if triggered can rampantly multiply out of control? [1] A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole," a property called self-similarity. The term was coined by Beno?t Mandelbrot in 1975 and was derived from the Latin fractus meaning "broken" or "fractured." A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion. http://www.kwsi.com/ynhti2009/image01.html A fractal often has the following features: 1. It has a fine structure at arbitrarily small scales.
    [Show full text]
  • Iterated Function System
    IARJSET ISSN (Online) 2393-8021 ISSN (Print) 2394-1588 International Advanced Research Journal in Science, Engineering and Technology ISO 3297:2007 Certified Vol. 3, Issue 8, August 2016 Iterated Function System S. C. Shrivastava Department of Applied Mathematics, Rungta College of Engineering and Technology, Bhilai C.G., India Abstract: Fractal image compression through IFS is very important for the efficient transmission and storage of digital data. Fractal is made up of the union of several copies of itself and IFS is defined by a finite number of affine transformation which characterized by Translation, scaling, shearing and rotat ion. In this paper we describe the necessary conditions to form an Iterated Function System and how fractals are generated through affine transformations. Keywords: Iterated Function System; Contraction Mapping. 1. INTRODUCTION The exploration of fractal geometry is usually traced back Metric Spaces definition: A space X with a real-valued to the publication of the book “The Fractal Geometry of function d: X × X → ℜ is called a metric space (X, d) if d Nature” [1] by the IBM mathematician Benoit B. possess the following properties: Mandelbrot. Iterated Function System is a method of constructing fractals, which consists of a set of maps that 1. d(x, y) ≥ 0 for ∀ x, y ∈ X explicitly list the similarities of the shape. Though the 2. d(x, y) = d(y, x) ∀ x, y ∈ X formal name Iterated Function Systems or IFS was coined 3. d x, y ≤ d x, z + d z, y ∀ x, y, z ∈ X . (triangle by Barnsley and Demko [2] in 1985, the basic concept is inequality).
    [Show full text]