Extracting knowledge from time series : an introduction to nonlinear empirical modeling

Resource Information

The work Extracting knowledge from time series : an introduction to nonlinear empirical modeling represents a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool. This resource is a combination of several types including: Work, Language Material, Books.

Label

Extracting knowledge from time series : an introduction to nonlinear empirical modeling

Title remainder

an introduction to nonlinear empirical modeling

Statement of responsibility

Boris P. Bezruchko, Dmitry A. Smirnov

Creator

• Bezruchko, Boris P

Contributor

• Smirnov, Dmitry A

Subject

• Synergetics
• Chaotic behavior in systems -- Mathematical models

Language

eng

Summary

This book addresses the fundamental question of how to construct mathematical models for the evolution of dynamical systems from experimentally-obtained time series. It places emphasis on chaotic signals and nonlinear modeling and discusses different approaches to the forecast of future system evolution. In particular, it teaches readers how to construct difference and differential model equations depending on the amount of a priori information that is available on the system in addition to the experimental data sets. This book will benefit graduate students and researchers from all natural sc

Member of

• Springer series in synergetics (Unnumbered)
• Online access with purchase: Springer
• Springer complexity

Cataloging source

GW5XE

Dewey number

003.857

Illustrations

illustrations

Index

index present

LC call number

Q172.5.C45

LC item number

B49 2010

Literary form

non fiction

Nature of contents

• dictionaries
• bibliography

Series statement

• Springer complexity
• Springer series in synergetics,