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1994 | Buch | 2. Auflage

Chaos, Fractals, and Noise

Stochastic Aspects of Dynamics

verfasst von: Andrzej Lasota, Michael C. Mackey

Verlag: Springer New York

Buchreihe : Applied Mathematical Sciences

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SUCHEN

Über dieses Buch

The first edition of this book was originally published in 1985 under the ti­ tle "Probabilistic Properties of Deterministic Systems. " In the intervening years, interest in so-called "chaotic" systems has continued unabated but with a more thoughtful and sober eye toward applications, as befits a ma­ turing field. This interest in the serious usage of the concepts and techniques of nonlinear dynamics by applied scientists has probably been spurred more by the availability of inexpensive computers than by any other factor. Thus, computer experiments have been prominent, suggesting the wealth of phe­ nomena that may be resident in nonlinear systems. In particular, they allow one to observe the interdependence between the deterministic and probabilistic properties of these systems such as the existence of invariant measures and densities, statistical stability and periodicity, the influence of stochastic perturbations, the formation of attractors, and many others. The aim of the book, and especially of this second edition, is to present recent theoretical methods which allow one to study these effects. We have taken the opportunity in this second edition to not only correct the errors of the first edition, but also to add substantially new material in five sections and a new chapter.

Inhaltsverzeichnis

Frontmatter
1. Introduction
Abstract
We begin by showing how densities may arise from the operation of a one- dimensional discrete time system and how the study of such systems can be facilitated by the use of densities.
Andrzej Lasota, Michael C. Mackey
2. The Toolbox
Abstract
In this and the following chapter, we introduce basic concepts necessary for understanding the flow of densities. These concepts may be studied in detail before continuing on to the core of our subject matter, which starts in Chapter 4, or, they may be skimmed on first reading to fix the location of important concepts for later reference.
Andrzej Lasota, Michael C. Mackey
3. Markov and Frobenius-Perron Operators
Abstract
Taking into account the concepts of the preceding chapter, we are now ready to formally introduce the Frobenius—Perron operator, which, as we saw in Chapter 1, is of considerable use in studying the evolution of densities under the operation of deterministic systems.
Andrzej Lasota, Michael C. Mackey
4. Studying Chaos with Densities
Abstract
Here we introduce the concept of measure-preserving transformations and then define and illustrate three levels of irregular behavior that such transformations can display. These three levels are known as ergodicity, mixing, and exactness. The central theme of the chapter is to show the utility of the Frobenius–Perron and Koopman operators in the study of these behaviors.
Andrzej Lasota, Michael C. Mackey
5. The Asymptotic Properties of Densities
Abstract
The preceding chapter was devoted to an examination of the various degrees of “chaotic” behavior (ergodicity, mixing, and exactness) that measure- preserving transformations may display. In particular, we saw the usefulness of the Koopman and Frobenius-Perron operators in answering these questions.
Andrzej Lasota, Michael C. Mackey
6. The Behavior of Transformations on Intervals and Manifolds
Abstract
This chapter is devoted to a series of examples of transformations on intervals and manifolds whose asymptotic behavior can be explored through the use of the material developed in Chapter 5 Although results are often stated in terms of the asymptotic stability of {P n }, where P is a Frobenius—Perron operator corresponding to a transformation S, remember that, according to Proposition 5.6.2, S is exact when {P n } is asymptotically stable and S is measure preserving.
Andrzej Lasota, Michael C. Mackey
7. Continuous Time Systems: An Introduction
Abstract
In previous chapters we concentrated on discrete time systems because they offer a convenient way of introducing many concepts and techniques of importance in the study of irregular behaviors in model systems. Now we turn to a study of continuous time systems.
Andrzej Lasota, Michael C. Mackey
8. Discrete Time Processes Embedded in Continuous Time Systems
Abstract
In this chapter, our goal is to introduce a way in which discrete time processes may be embedded in continuous time systems without altering the phase space. To do this, we adopt a strictly probabilistic point of view, not embedding the deterministic system S: X → X in a continuous time process, but rather embedding its Frobenius-Perron operator P:L 1 (X) →L 1 (X) that acts on L 1 functions. The result of this embedding is an abstract form of the Boltzmann equation. This chapter requires some elementary definitions from probability theory and a knowledge of Poisson processes, which are introduced following the preliminary remarks of the next section.
Andrzej Lasota, Michael C. Mackey
9. Entropy
Abstract
The concept of entropy was first introduced by Clausius and later used in a different form by L. Boltzmann in his pioneering work on the kinetic theory of gases published in 1866. Since then, entropy has played a pivotal role in the development of many areas in physics and chemistry and has had important ramifications in ergodic theory. However, the Boltzmann entropy is different from the Kolmogorov-Sinai-Ornstein entropy [Walters, 1975; Parry, 1981] that has been so successfully used in solving the problem of isomorphism of dynamical systems, and which is related to the work of Shannon [see Shannon and Weaver, 1949].
Andrzej Lasota, Michael C. Mackey
10. Stochastic Perturbation of Discrete Time Systems
Abstract
We have seen two ways in which uncertainty (and thus probability) may appear in the study of strictly deterministic systems. The first was the consequence of following a random distribution of initial states, which, in turn, led to a development of the notion of the Frobenius-Perron operator and an examination of its properties as a means of studying the asymptotic properties of flows of densities. The second resulted from the random application of a transformation S to a system and led naturally to our study of the linear Boltzmann equations.
Andrzej Lasota, Michael C. Mackey
11. Stochastic Perturbation of Continuous Time Systems
Abstract
In this chapter continuous time systems in the presence of noise are considered. This leads us to examine systems of stochastic differential equations and to a derivation of the forward Fokker-Planck equation, describing the evolution of densities for these systems. We close with some results concerning the asymptotic stability of solutions to the Fokker-Planck equation.
Andrzej Lasota, Michael C. Mackey
12. Markov and Foias Operators
Abstract
Throughout this book we have studied the asymptotic behavior of densities. However, in some cases the statistical properties of dynamical systems are better described if we use a more general notion than a density, namely, a measure. In fact, the sequences (or flows) of measures generated by dynamical systems simultaneously generalize the notion of trajectories and the sequences (or flows) of densities. They are of particular value in studying fractals.
Andrzej Lasota, Michael C. Mackey
Backmatter
Metadaten
Titel
Chaos, Fractals, and Noise
verfasst von
Andrzej Lasota
Michael C. Mackey
Copyright-Jahr
1994
Verlag
Springer New York
Electronic ISBN
978-1-4612-4286-4
Print ISBN
978-1-4612-8723-0
DOI
https://doi.org/10.1007/978-1-4612-4286-4