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Bezruchko, Boris P.; Smirnov, Dmitry A. - Extracting Knowledge From Time Series - 9783642264825 - V9783642264825
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Extracting Knowledge From Time Series

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Description for Extracting Knowledge From Time Series Paperback. This book, a useful self-study guide, addresses the fundamental question on how to construct mathematical models for the evolution of dynamical systems from experimentally obtained time series. There is emphasis is on chaotic signals and nonlinear modelling. Series: Springer Series in Synergetics. Num Pages: 432 pages, 162 black & white illustrations, biography. BIC Classification: KCH; KF; PHS; PHVG; TQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 22. Weight in Grams: 603.
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” ... Read more

Product Details

Format
Paperback
Publication date
2012
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
432
Condition
New
Series
Springer Series in Synergetics
Number of Pages
410
Place of Publication
Berlin, Germany
ISBN
9783642264825
SKU
V9783642264825
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

Reviews for Extracting Knowledge From Time Series
From the reviews: “Extracting knowledge from time series is a very neat title–it exactly encapsulates the topic which the authors hope to cover in this volume. … This is admirable, and the result is valuable. … This is overall a useful volume for providing an overview of the area … .” (Michael Small, Mathematical Reviews, Issue 2012 d) ... Read more

Goodreads reviews for Extracting Knowledge From Time Series


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