Overview

This book develops a clear and systematic treatment of time series of data, regular and chaotic, that one finds in observations of nonlinear systems. The reader is led 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. The text 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 the modern mathematical tools for investigating chaotic behavior to uncover properties of physical systems. The methods require knowledge of dynamical systems at the advanced undergraduate level and some knowledge of Fourier transforms and other signal processing methods. The toolkit developed in the book will provide the reader with efficient and effective methods for analyzing signals from nonlinear sources; these methods are applicable to problems of control, communication, and prediction in a wide variety of systems encountered in physics, chemistry, biology, and geophysics.

Full Product Details

Author:   David Aldous ,  Persi Diaconis ,  Joel Spencer ,  J. Michael Steele
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   1995 ed.
Volume:   72
Dimensions:   Width: 15.50cm , Height: 1.10cm , Length: 23.50cm
Weight:   0.960kg
ISBN:  

9780387945323

ISBN 10:   0387945326
Pages:   158
Publication Date:   29 June 1995
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

On simulating a Markov chain stationary distribution when transition probabilities are unknown.- A note on network reliability.- Rectangular arrays with fixed margins.- Three examples of Monte-Carlo Markov chains: at the interface between statistical computing, computer science, and statistical mechanics.- The move-to-front rule for self-organizing lists with Markov dependent requests.- The asymptotic lower bound on the diagonal Ramsey numbers: A closer look.- Random walks and undirected graph connectivity: A survey.- Sidon sets with small gaps.- Variations on the monotone subsequence theme of Erd?s and Szekeres.- Randomised approximation schemes for Tutte-Gröthendieck invariants.- Quasi-additive Euclidean functionals.

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