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.
FREE Two-Day Shipping for students on millions of items. Learn more |
| |||||||||||||||
Book Description
Frequently Bought Together
Product Details
Would you like to update product info or give feedback on images?
|
More About the Author
Discover books, learn about writers, read author blogs, and more.
Customer Reviews
|
Share your thoughts with other customers:
|
||||||||||||||||||||||
|
Most Helpful Customer Reviews
10 of 12 people found the following review helpful:
5.0 out of 5 stars
Simply pure scientific writing,
By
This review is from: Analysis of Observed Chaotic Data (Institute for Nonlinear Science) (Paperback)
It's probably the best-written book i've read so far: it's what i call scientific writing. No approximate statement, every concept is provided with a discussion of what it can be used for and what it cannot. There is no tedious statement like the one you see in many science books where the theory is simply given without any mention of the underlying hypothesis making it useless in practice...time series analysts should know what i'm talking about...Of course, reading this book when you don't have any interest in time series analysis is probably a bad idea since you need quite a strong background in classical time series analysis techniques (time as well as frequency domain).
Share your thoughts with other customers: Create your own review
|
|
Inside This Book
(learn more)
Key Phrases - Statistically Improbable Phrases (SIPs):
(learn more)
laser chaos, local false neighbors, fluid boundary layer, mutual false nearest neighbors, prediction set, predicted intensity, using local linear maps, total infrared intensity, global false nearest neighbors, wall pressure data, local false nearest neighbors, hysteretic circuit, coefficients for the local maps, backward exponents, unsynchronized motions, time delay space, time delay reconstruction, local linear predictor, laser intensity fluctuations, time delay vectors, hysteretic element, global exponents, average mutual information, magnetoelastic ribbon, reconstructed phase space Key Phrases - Capitalized Phrases (CAPs): (learn more)
Chaotic Laser Intensity Fluctuations, Synchronization of Chaotic Systems, The Fourier, Great Salt Lake, Lorenz Model, Number of Steps, Prediction Type, Cramér-Rao Bounds, Invariants of the Motion, Other Example Systems, Modeling Chaos, Step Prediction, Naval Research Laboratory, Correlation Function Derivative, Machine Tools, Choosing Time Delays, Examples of Control, Reconstruction of Phase Space, Observed Intensity, Bad Predictions, Zero Exponent Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
laser chaos, local false neighbors, fluid boundary layer, mutual false nearest neighbors, prediction set, predicted intensity, using local linear maps, total infrared intensity, global false nearest neighbors, wall pressure data, local false nearest neighbors, hysteretic circuit, coefficients for the local maps, backward exponents, unsynchronized motions, time delay space, time delay reconstruction, local linear predictor, laser intensity fluctuations, time delay vectors, hysteretic element, global exponents, average mutual information, magnetoelastic ribbon, reconstructed phase space Key Phrases - Capitalized Phrases (CAPs): (learn more)
Chaotic Laser Intensity Fluctuations, Synchronization of Chaotic Systems, The Fourier, Great Salt Lake, Lorenz Model, Number of Steps, Prediction Type, Cramér-Rao Bounds, Invariants of the Motion, Other Example Systems, Modeling Chaos, Step Prediction, Naval Research Laboratory, Correlation Function Derivative, Machine Tools, Choosing Time Delays, Examples of Control, Reconstruction of Phase Space, Observed Intensity, Bad Predictions, Zero Exponent Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
54
books
cite this book:
See all 54 books citing this book
- Nonlinear Dynamics and Statistics by Alistair I. Mees on 4 pages
- Modelling and Forecasting Financial Data: Techniques of Nonlinear Dynamics (Studies in Computational Finance, Volume 2) by Abdol S. Soofi on page 39, page 157, and page 415
- Controlling Chaos and Bifurcations in Engineering Systems by Guanrong Chen on page 19, and page 475
- Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers by Robert Hilborn on page 422, and Back Matter
- Advances in Kernel Methods: Support Vector Learning by Bernhard Schölkopf in Back Matter
See all 54 books citing this book
Tags Customers Associate with This Product(What's this?)Click on a tag to find related items, discussions, and people.
|
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
Customer Discussions
|
This product's forum
Active discussions in related forums
Search Customer Discussions
|
Related forums
|
