| Author: |
|
Boris P. Bezruchko
|
| Title: |
 |
Extracting Knowledge From Time Series: An Introduction to Nonlinear Empirical Modeling (Springer Series in Synergetics) |
| Moochable copies: |
|
No copies available |
| Topics: |
|
| Published in: |
|
German (Germany) |
| Binding: |
|
Hardcover |
| Pages: |
|
410 |
| Date: |
|
2010-09-07 |
| ISBN: |
|
3642126006 |
| Publisher: |
|
Springer |
| Size: |
|
1.06 x 6.36 x 9.46 inches |
| Edition: |
|
2010 |
|
|
|
| Description: |
|
Product Description
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.
|
| URL: |
|
http://bookmooch.com/3642126006 |
|
|
