Extracting Knowledge From Time Series

An Introduction to Nonlinear Empirical Modeling

Nonfiction, Science & Nature, Science, Earth Sciences, Geophysics, Physics, General Physics
Cover of the book Extracting Knowledge From Time Series by Boris P. Bezruchko, Dmitry A. Smirnov, Springer Berlin Heidelberg
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Boris P. Bezruchko, Dmitry A. Smirnov ISBN: 9783642126017
Publisher: Springer Berlin Heidelberg Publication: September 5, 2010
Imprint: Springer Language: English
Author: Boris P. Bezruchko, Dmitry A. Smirnov
ISBN: 9783642126017
Publisher: Springer Berlin Heidelberg
Publication: September 5, 2010
Imprint: Springer
Language: English

Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.

More books from Springer Berlin Heidelberg

Cover of the book The Development of Metalinguistic Abilities in Children by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Towards a Dynamic Regional Innovation System by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Quantum Mechanics by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II) by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Robuste Regelung by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book A Measure Theoretical Approach to Quantum Stochastic Processes by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Handbuch Industrie 4.0 Bd.3 by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Green Building by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Nanostructured Materials for Next-Generation Energy Storage and Conversion by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Architecture Principles by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book CMOS Cantilever Sensor Systems by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Stochastic Calculus with Infinitesimals by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Testing Molecular Wires by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Industrial and Technological Applications of Transport in Porous Materials by Boris P. Bezruchko, Dmitry A. Smirnov
Cover of the book Fertigungsverfahren 1 by Boris P. Bezruchko, Dmitry A. Smirnov
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy