Quantifying Chaos from Time-series Data Through Lyapunov Exponents
Quantifying Chaos from Time-series Data Through Lyapunov Exponents

Author: Julio Emilio Sandubete Galán

Publisher:

Published: 2020

Total Pages:

ISBN-13:

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Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic dynamic systems which can behave like an apparently erratic and irregular motion. Keep in mind that if we could characterise a chaotic system in some sense it would allow us to evidence that a deterministic generating system exists behind that chaotic system in spite of showing an apparently random behaviour. This fact would provide us to take advantage of this deterministic character to be able to make predictions and control over the variables of these (chaotic) deterministic dynamic systems. Methods and techniques related to test the hypothesis of chaos try to estimate the so-called Lyapunov exponents as a way of characterising achaotic system. Nowadays quantifying chaos from time-series data through this kind of quantitative measure in a rigorous fashion is far from being a trivial exercise and poses a number of theoretical and practical challenges...

Exploring Chaos
Exploring Chaos

Author: Brian Davies

Publisher: CRC Press

Published: 2018-05-04

Total Pages: 200

ISBN-13: 0429982496

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This book presents elements of the theory of chaos in dynamical systems in a framework of theoretical understanding coupled with numerical and graphical experimentation. It describes the theory of fractals, focusing on the importance of scaling and ordinary differential equations.

Predictability of Chaotic Dynamics
Predictability of Chaotic Dynamics

Author: Juan C. Vallejo

Publisher: Springer

Published: 2017-03-27

Total Pages: 147

ISBN-13: 3319518933

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This book is primarily concerned with the computational aspects of predictability of dynamical systems – in particular those where observation, modeling and computation are strongly interdependent. Unlike with physical systems under control in laboratories, for instance in celestial mechanics, one is confronted with the observation and modeling of systems without the possibility of altering the key parameters of the objects studied. Therefore, the numerical simulations offer an essential tool for analyzing these systems. With the widespread use of computer simulations to solve complex dynamical systems, the reliability of the numerical calculations is of ever-increasing interest and importance. This reliability is directly related to the regularity and instability properties of the modeled flow. In this interdisciplinary scenario, the underlying physics provide the simulated models, nonlinear dynamics provides their chaoticity and instability properties, and the computer sciences provide the actual numerical implementation. This book introduces and explores precisely this link between the models and their predictability characterization based on concepts derived from the field of nonlinear dynamics, with a focus on the finite-time Lyapunov exponents approach. The method is illustrated using a number of well-known continuous dynamical systems, including the Contopoulos, Hénon-Heiles and Rössler systems. To help students and newcomers quickly learn to apply these techniques, the appendix provides descriptions of the algorithms used throughout the text and details how to implement them in order to solve a given continuous dynamical system.

Chaos And Forecasting - Proceedings Of The Royal Society Discussion Meeting
Chaos And Forecasting - Proceedings Of The Royal Society Discussion Meeting

Author: Howell A M Tong

Publisher: World Scientific

Published: 1995-04-26

Total Pages: 358

ISBN-13: 9814549762

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It is now generally recognised that very simple dynamical systems can produce apparently random behaviour. In the last couple of years, attention has turned to focus on the flip side of this coin: random-looking time series (or random-looking patterns in space) may indeed be the result of very complicated processes or “real noise”, but they may equally well be produced by some very simple mechanism (a low-dimensional attractor). In either case, a long-term prediction will be possible only in probabilistic terms. However, in the very short term, random systems will still be unpredictable but low-dimensional chaotic ones may be predictable (appearances to the contrary). The Royal Society held a two-day discussion meeting on topics covering diverse fields, including biology, economics, geophysics, meteorology, statistics, epidemiology, earthquake science and many others. Each topic was covered by a leading expert in the field. The meeting dealt with different basic approaches to the problem of chaos and forecasting, and covered applications to nonlinear forecasting of both artificially-generated time series and real data from context in the above-mentioned diverse fields. This book marks a rather special and rare occasion on which prominent scientists from different areas converge on the same theme. It forms an informative introduction to the science of chaos, with special reference to real data.

Analysis of Observed Chaotic Data
Analysis of Observed Chaotic Data

Author: Henry Abarbanel

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 278

ISBN-13: 1461207630

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A clear and systematic treatment of time series of data, regular and chaotic, found in nonlinear systems. The text leads readers from measurements of one or more variables through the steps of building models of the source as a dynamical system, classifying the source by its dynamical characteristics, and finally predicting and controlling the dynamical system. It examines methods for separating the signal of physical interest from contamination by unwanted noise, and for investigating the phase space of the chaotic signal and its properties. The emphasis throughout is on the use of modern mathematical tools for investigating chaotic behaviour to uncover properties of physical systems, requiring knowledge of dynamical systems at the advanced undergraduate level and some knowledge of Fourier transforms and other signal processing methods.

Chaos: A Statistical Perspective
Chaos: A Statistical Perspective

Author: Kung-Sik Chan

Publisher: Springer Science & Business Media

Published: 2001-08-09

Total Pages: 326

ISBN-13: 9780387952802

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This book discusses dynamical systems that are typically driven by stochastic dynamic noise. It is written by two statisticians essentially for the statistically inclined readers. It covers many of the contributions made by the statisticians in the past twenty years or so towards our understanding of estimation, the Lyapunov-like index, the nonparametric regression, and many others, many of which are motivated by their dynamical system counterparts but have now acquired a distinct statistical flavor.

Experimental Chaos - Proceedings Of The 3rd Conference
Experimental Chaos - Proceedings Of The 3rd Conference

Author: Robert G Harrison

Publisher: World Scientific

Published: 1996-10-16

Total Pages: 342

ISBN-13: 981454776X

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This volume, the third in our unique series on experimental chaos, brings together from a broad range of disciplines, some of the exciting developments of the last two years concerned with the observations, measurements and applications of nonlinear dynamical behaviour. Included are chaos, spatio-temporal chaos and patterns, control of chaos, time series analysis and characterization, pattern recognitions and signal processing. The subjects covered include optics, fluids, condensed matter, astrophysics, biological, chemical and medical sciences, engineering, metreorology and oceanography.

Handbook of Applications of Chaos Theory
Handbook of Applications of Chaos Theory

Author: Christos H. Skiadas

Publisher: CRC Press

Published: 2017-12-19

Total Pages: 921

ISBN-13: 1315356546

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In addition to explaining and modeling unexplored phenomena in nature and society, chaos uses vital parts of nonlinear dynamical systems theory and established chaotic theory to open new frontiers and fields of study. Handbook of Applications of Chaos Theory covers the main parts of chaos theory along with various applications to diverse areas. Expert contributors from around the world show how chaos theory is used to model unexplored cases and stimulate new applications. Accessible to scientists, engineers, and practitioners in a variety of fields, the book discusses the intermittency route to chaos, evolutionary dynamics and deterministic chaos, and the transition to phase synchronization chaos. It presents important contributions on strange attractors, self-exciting and hidden attractors, stability theory, Lyapunov exponents, and chaotic analysis. It explores the state of the art of chaos in plasma physics, plasma harmonics, and overtone coupling. It also describes flows and turbulence, chaotic interference versus decoherence, and an application of microwave networks to the simulation of quantum graphs. The book proceeds to give a detailed presentation of the chaotic, rogue, and noisy optical dissipative solitons; parhelic-like circle and chaotic light scattering; and interesting forms of the hyperbolic prism, the Poincaré disc, and foams. It also covers numerous application areas, from the analysis of blood pressure data and clinical digital pathology to chaotic pattern recognition to economics to musical arts and research.

Chaos
Chaos

Author: A.A. Tsonis

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 279

ISBN-13: 1461533600

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Based on chaos theory two very important points are clear: (I) random looking aperiodic behavior may be the product of determinism, and (2) nonlinear problems should be treated as nonlinear problems and not as simplified linear problems. The theoretical aspects ofchaos have been presented in great detail in several excellent books published in the last five years or so. However, while the problems associated with applications of the theory-such as dimension and Lyapunov exponentsestimation, chaosand nonlinear pre diction, and noise reduction-have been discussed in workshops and ar ticles, they have not been presented in book form. This book has been prepared to fill this gap between theory and ap plicationsand to assist studentsand scientists wishingto apply ideas from the theory ofnonlinear dynamical systems to problems from their areas of interest. The book is intended to be used as a text for an upper-level undergraduate or graduate-level course, as well as a reference source for researchers. My philosophy behind writing this book was to keep it simple and informative without compromising accuracy. I have made an effort to presentthe conceptsby usingsimplesystemsand step-by-stepderivations. Anyone with an understanding ofbasic differential equations and matrix theory should follow the text without difficulty. The book was designed to be self-contained. When applicable, examples accompany the theory. The reader will notice, however, that in the later chapters specific examples become less frequent. This is purposely done in the hope that individuals will draw on their own ideas and research projects for examples.