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What Is a Complex Adaptive System?
PROJECT GUTS What is a Complex Adaptive System? Introduction During the last three decades a leap has been made from the application of computing to help scientists ‘do’ science to the integration of computer science concepts, tools and theorems into the very fabric of science. The modeling of complex adaptive systems (CAS) is an example of such an integration of computer science into the very fabric of science; models of complex systems are used to understand, predict and prevent the most daunting problems we face today; issues such as climate change, loss of biodiversity, energy consumption and virulent disease affect us all. The study of complex adaptive systems, has come to be seen as a scientific frontier, and an increasing ability to interact systematically with highly complex systems that transcend separate disciplines will have a profound affect on future science, engineering and industry as well as in the management of our planet’s resources (Emmott et al., 2006). The name itself, “complex adaptive systems” conjures up images of complicated ideas that might be too difficult for a novice to understand. Instead, the study of CAS does exactly the opposite; it creates a unified method of studying disparate systems that elucidates the processes by which they operate. A complex system is simply a system in which many independent elements or agents interact, leading to emergent outcomes that are often difficult (or impossible) to predict simply by looking at the individual interactions. The “complex” part of CAS refers in fact to the vast interconnectedness of these systems. Using the principles of CAS to study these topics as related disciplines that can be better understood through the application of models, rather than a disparate collection of facts can strengthen learners’ understanding of these topics and prepare them to understand other systems by applying similar methods of analysis (Emmott et al., 2006). -
Chapter 6: Ensemble Forecasting and Atmospheric Predictability
Chapter 6: Ensemble Forecasting and Atmospheric Predictability Introduction Deterministic Chaos (what!?) In 1951 Charney indicated that forecast skill would break down, but he attributed it to model errors and errors in the initial conditions… In the 1960’s the forecasts were skillful for only one day or so. Statistical prediction was equal or better than dynamical predictions, Like it was until now for ENSO predictions! Lorenz wanted to show that statistical prediction could not match prediction with a nonlinear model for the Tokyo (1960) NWP conference So, he tried to find a model that was not periodic (otherwise stats would win!) He programmed in machine language on a 4K memory, 60 ops/sec Royal McBee computer He developed a low-order model (12 d.o.f) and changed the parameters and eventually found a nonperiodic solution Printed results with 3 significant digits (plenty!) Tried to reproduce results, went for a coffee and OOPS! Lorenz (1963) discovered that even with a perfect model and almost perfect initial conditions the forecast loses all skill in a finite time interval: “A butterfly in Brazil can change the forecast in Texas after one or two weeks”. In the 1960’s this was only of academic interest: forecasts were useless in two days Now, we are getting closer to the 2 week limit of predictability, and we have to extract the maximum information Central theorem of chaos (Lorenz, 1960s): a) Unstable systems have finite predictability (chaos) b) Stable systems are infinitely predictable a) Unstable dynamical system b) Stable dynamical -
SUBFRACTALS INDUCED by SUBSHIFTS a Dissertation
SUBFRACTALS INDUCED BY SUBSHIFTS A Dissertation Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science By Elizabeth Sattler In Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Major Department: Mathematics May 2016 Fargo, North Dakota NORTH DAKOTA STATE UNIVERSITY Graduate School Title SUBFRACTALS INDUCED BY SUBSHIFTS By Elizabeth Sattler The supervisory committee certifies that this dissertation complies with North Dakota State Uni- versity's regulations and meets the accepted standards for the degree of DOCTOR OF PHILOSOPHY SUPERVISORY COMMITTEE: Dr. Do˘ganC¸¨omez Chair Dr. Azer Akhmedov Dr. Mariangel Alfonseca Dr. Simone Ludwig Approved: 05/24/2016 Dr. Benton Duncan Date Department Chair ABSTRACT In this thesis, a subfractal is the subset of points in the attractor of an iterated function system in which every point in the subfractal is associated with an allowable word from a subshift on the underlying symbolic space. In the case in which (1) the subshift is a subshift of finite type with an irreducible adjacency matrix, (2) the iterated function system satisfies the open set condition, and (3) contractive bounds exist for each map in the iterated function system, we find bounds for both the Hausdorff and box dimensions of the subfractal, where the bounds depend both on the adjacency matrix and the contractive bounds on the maps. We extend this result to sofic subshifts, a more general subshift than a subshift of finite type, and to allow the adjacency matrix n to be reducible. The structure of a subfractal naturally defines a measure on R . -
Object Oriented Programming
No. 52 March-A pril'1990 $3.95 T H E M TEe H CAL J 0 URN A L COPIA Object Oriented Programming First it was BASIC, then it was structures, now it's objects. C++ afi<;ionados feel, of course, that objects are so powerful, so encompassing that anything could be so defined. I hope they're not placing bets, because if they are, money's no object. C++ 2.0 page 8 An objective view of the newest C++. Training A Neural Network Now that you have a neural network what do you do with it? Part two of a fascinating series. Debugging C page 21 Pointers Using MEM Keep C fro111 (C)rashing your system. An AT Keyboard Interface Use an AT keyboard with your latest project. And More ... Understanding Logic Families EPROM Programming Speeding Up Your AT Keyboard ((CHAOS MADE TO ORDER~ Explore the Magnificent and Infinite World of Fractals with FRAC LS™ AN ELECTRONIC KALEIDOSCOPE OF NATURES GEOMETRYTM With FracTools, you can modify and play with any of the included images, or easily create new ones by marking a region in an existing image or entering the coordinates directly. Filter out areas of the display, change colors in any area, and animate the fractal to create gorgeous and mesmerizing images. Special effects include Strobe, Kaleidoscope, Stained Glass, Horizontal, Vertical and Diagonal Panning, and Mouse Movies. The most spectacular application is the creation of self-running Slide Shows. Include any PCX file from any of the popular "paint" programs. FracTools also includes a Slide Show Programming Language, to bring a higher degree of control to your shows. -
March 12, 2011 DIAGONALLY NON-RECURSIVE FUNCTIONS and EFFECTIVE HAUSDORFF DIMENSION 1. Introduction Reimann and Terwijn Asked Th
March 12, 2011 DIAGONALLY NON-RECURSIVE FUNCTIONS AND EFFECTIVE HAUSDORFF DIMENSION NOAM GREENBERG AND JOSEPH S. MILLER Abstract. We prove that every sufficiently slow growing DNR function com- putes a real with effective Hausdorff dimension one. We then show that for any recursive unbounded and nondecreasing function j, there is a DNR function bounded by j that does not compute a Martin-L¨ofrandom real. Hence there is a real of effective Hausdorff dimension 1 that does not compute a Martin-L¨of random real. This answers a question of Reimann and Terwijn. 1. Introduction Reimann and Terwijn asked the dimension extraction problem: can one effec- tively increase the information density of a sequence with positive information den- sity? For a formal definition of information density, they used the notion of effective Hausdorff dimension. This effective version of the classical Hausdorff dimension of geometric measure theory was first defined by Lutz [10], using a martingale defini- tion of Hausdorff dimension. Unlike classical dimension, it is possible for singletons to have positive dimension, and so Lutz defined the dimension dim(A) of a binary sequence A 2 2! to be the effective dimension of the singleton fAg. Later, Mayor- domo [12] (but implicit in Ryabko [15]), gave a characterisation using Kolmogorov complexity: for all A 2 2!, K(A n) C(A n) dim(A) = lim inf = lim inf ; n!1 n n!1 n where C is plain Kolmogorov complexity and K is the prefix-free version.1 Given this formal notion, the dimension extraction problem is the following: if 2 dim(A) > 0, is there necessarily a B 6T A such that dim(B) > dim(A)? The problem was recently solved by the second author [13], who showed that there is ! an A 2 2 such that dim(A) = 1=2 and if B 6T A, then dim(B) 6 1=2. -
Living Systems Theory (Under Construction)
Calhoun: The NPS Institutional Archive Faculty and Researcher Publications Faculty and Researcher Publications 2008-06-30 Living Systems Theory (under construction) Bradley, Gordon http://hdl.handle.net/10945/38246 Living Systems Theory (under construction) Agranoff, B. W. (2003). Obituaries: James Grier Miller. Retrieved May 26, 2008, from University of Michigan: The University Record Online: http://www.ur.umich.edu/0203/Feb03_03/obits.shtml Bertalanffy, Ludwig von. (1968). General System Theory: Foundations, Development, Applications. New York: George Braziller. Crawford, Raymond R. (1981). An Application of Living Systems Theory to Combat Models. Master’s Thesis. Monterey, CA: Naval Postgraduate School. http://stinet.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA101103 Duncan, Daniel M. (1972). James G. Miller's Living Systems Theory: Issues for Management Thought and Practice. The Academy of Management Journal, 15:4, 513-523. http://www.jstor.org/stable/255145?seq=1 Kuhn, Alfred. (1979). Differences vs. Similarities in Living Systems. Contemporary Sociology, 8:5, 691- 696. http://www.jstor.org/sici?sici=0094- 3061(197909)8%3A5%3C691%3ADVSILS%3E2.0.CO%3B2-4 Miller, James Grier. (1979). Response to the Reviewers of Living Systems. Contemporary Sociology, 8:5, 705-715. http://www.jstor.org/sici?sici=0094- 3061(197909)8%3A5%3C705%3ARTTROL%3E2.0.CO%3B2-8 Miller, James Grier. (1995). Living Systems. Niwot: University of Colorado. (out of print) Oliva, Terence A. and R. Eric Reidenbach. (1981). General Living Systems Theory and Marketing: A Framework for Analysis. Journal of Marketing, 45:4, 30-37. http://www.jstor.org/sici?sici=0022- 2429(198123)45%3A4%3C30%3AGLSTAM%3E2.0.CO%3B2-R Parent, Elaine. -
A Gentle Introduction to Dynamical Systems Theory for Researchers in Speech, Language, and Music
A Gentle Introduction to Dynamical Systems Theory for Researchers in Speech, Language, and Music. Talk given at PoRT workshop, Glasgow, July 2012 Fred Cummins, University College Dublin [1] Dynamical Systems Theory (DST) is the lingua franca of Physics (both Newtonian and modern), Biology, Chemistry, and many other sciences and non-sciences, such as Economics. To employ the tools of DST is to take an explanatory stance with respect to observed phenomena. DST is thus not just another tool in the box. Its use is a different way of doing science. DST is increasingly used in non-computational, non-representational, non-cognitivist approaches to understanding behavior (and perhaps brains). (Embodied, embedded, ecological, enactive theories within cognitive science.) [2] DST originates in the science of mechanics, developed by the (co-)inventor of the calculus: Isaac Newton. This revolutionary science gave us the seductive concept of the mechanism. Mechanics seeks to provide a deterministic account of the relation between the motions of massive bodies and the forces that act upon them. A dynamical system comprises • A state description that indexes the components at time t, and • A dynamic, which is a rule governing state change over time The choice of variables defines the state space. The dynamic associates an instantaneous rate of change with each point in the state space. Any specific instance of a dynamical system will trace out a single trajectory in state space. (This is often, misleadingly, called a solution to the underlying equations.) Description of a specific system therefore also requires specification of the initial conditions. In the domain of mechanics, where we seek to account for the motion of massive bodies, we know which variables to choose (position and velocity). -
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. -
Control Theory
Control theory S. Simrock DESY, Hamburg, Germany Abstract In engineering and mathematics, control theory deals with the behaviour of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain ref- erence over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. Rapid advances in digital system technology have radically altered the control design options. It has become routinely practicable to design very complicated digital controllers and to carry out the extensive calculations required for their design. These advances in im- plementation and design capability can be obtained at low cost because of the widespread availability of inexpensive and powerful digital processing plat- forms and high-speed analog IO devices. 1 Introduction The emphasis of this tutorial on control theory is on the design of digital controls to achieve good dy- namic response and small errors while using signals that are sampled in time and quantized in amplitude. Both transform (classical control) and state-space (modern control) methods are described and applied to illustrative examples. The transform methods emphasized are the root-locus method of Evans and fre- quency response. The state-space methods developed are the technique of pole assignment augmented by an estimator (observer) and optimal quadratic-loss control. The optimal control problems use the steady-state constant gain solution. Other topics covered are system identification and non-linear control. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. -
Adaptive Synchronization of Chaotic Systems and Its Application to Secure Communications
Chaos, Solitons and Fractals 11 (2000) 1387±1396 www.elsevier.nl/locate/chaos Adaptive synchronization of chaotic systems and its application to secure communications Teh-Lu Liao *, Shin-Hwa Tsai Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan, ROC Accepted 15 March 1999 Abstract This paper addresses the adaptive synchronization problem of the drive±driven type chaotic systems via a scalar transmitted signal. Given certain structural conditions of chaotic systems, an adaptive observer-based driven system is constructed to synchronize the drive system whose dynamics are subjected to the systemÕs disturbances and/or some unknown parameters. By appropriately selecting the observer gains, the synchronization and stability of the overall systems can be guaranteed by the Lyapunov approach. Two well-known chaotic systems: Rossler-like and Chua's circuit are considered as illustrative examples to demonstrate the eectiveness of the proposed scheme. Moreover, as an application, the proposed scheme is then applied to a secure communication system whose process consists of two phases: the adaptation phase in which the chaotic transmitterÕs disturbances are estimated; and the communication phase in which the information signal is transmitted and then recovered on the basis of the estimated parameters. Simulation results verify the proposed schemeÕs success in the communication application. Ó 2000 Elsevier Science Ltd. All rights reserved. 1. Introduction Synchronization in chaotic systems has received increasing attention [1±6] with several studies on the basis of theoretical analysis and even realization in laboratory having demonstrated the pivotal role of this phenomenon in secure communications [7±10]. The preliminary type of chaos synchronization consists of drive±driven systems: a drive system and a custom designed driven system. -
Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem
Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2014, Article ID 640764, 25 pages http://dx.doi.org/10.1155/2014/640764 Research Article Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem Hui Lu, Lijuan Yin, Xiaoteng Wang, Mengmeng Zhang, and Kefei Mao School of Electronic and Information Engineering, Beihang University, Beijing 100191, China Correspondence should be addressed to Hui Lu; [email protected] Received 13 March 2014; Revised 20 June 2014; Accepted 20 June 2014; Published 15 July 2014 Academic Editor: Jyh-Hong Chou Copyright © 2014 Hui Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Test task scheduling problem (TTSP) is a complex optimization problem and has many local optima. In this paper, a hybrid chaotic multiobjective evolutionary algorithm based on decomposition (CMOEA/D) is presented to avoid becoming trapped in local optima and to obtain high quality solutions. First, we propose an improving integrated encoding scheme (IES) to increase the efficiency. Then ten chaotic maps are applied into the multiobjective evolutionary algorithm based on decomposition (MOEA/D) in three phases, that is, initial population and crossover and mutation operators. To identify a good approach for hybrid MOEA/D and chaos and indicate the effectiveness of the improving IES several experiments are performed. The Pareto front and the statistical results demonstrate that different chaotic maps in different phases have different effects for solving the TTSP especially the circle map and ICMIC map. -
The Effects of Feedback Interventions on Performance: a Historical Review, a Meta-Analysis, and a Preliminary Feedback Intervention Theory
Psychological Bulletin Copyright 1996 by the American Psychological Association, Inc. 1996, Vol. II9, No. 2, 254-284 0033-2909/96/S3.00 The Effects of Feedback Interventions on Performance: A Historical Review, a Meta-Analysis, and a Preliminary Feedback Intervention Theory Avraham N. Kluger Angelo DeNisi The Hebrew University of Jerusalem Rutgers University Since the beginning of the century, feedback interventions (FIs) produced negative—but largely ignored—effects on performance. A meta-analysis (607 effect sizes; 23,663 observations) suggests that FIs improved performance on average (d = .41) but that over '/3 of the FIs decreased perfor- mance. This finding cannot be explained by sampling error, feedback sign, or existing theories. The authors proposed a preliminary FI theory (FIT) and tested it with moderator analyses. The central assumption of FIT is that FIs change the locus of attention among 3 general and hierarchically organized levels of control: task learning, task motivation, and meta-tasks (including self-related) processes. The results suggest that FI effectiveness decreases as attention moves up the hierarchy closer to the self and away from the task. These findings are further moderated by task characteristics that are still poorly understood. To relate feedback directly to behavior is very confusing. Results able has been historically disregarded by most FI researchers. This are contradictory and seldom straight-forward. (Ilgen, Fisher, & disregard has led to a widely shared assumption that FIs consis- Taylor, 1979, p. 368) tently improve performance. Fortunately, several FI researchers The effects of manipulation of KR [knowledge of results] on motor (see epigraphs) have recently recognized that FIs have highly vari- learning.