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Titlebook: Extracting Knowledge From Time Series; An Introduction to N Boris P. Bezruchko,Dmitry A. Smirnov Book 2010 Springer-Verlag Berlin Heidelber

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書目名稱Extracting Knowledge From Time Series
副標(biāo)題An Introduction to N
編輯Boris P. Bezruchko,Dmitry A. Smirnov
視頻videohttp://file.papertrans.cn/320/319956/319956.mp4
概述Useful as a self-study guide.Gives a modern approach and practical examples.Written by well known authors having made many contribution to the field.Includes supplementary material:
叢書名稱Springer Series in Synergetics
圖書封面Titlebook: Extracting Knowledge From Time Series; An Introduction to N Boris P. Bezruchko,Dmitry A. Smirnov Book 2010 Springer-Verlag Berlin Heidelber
描述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
出版日期Book 2010
關(guān)鍵詞Stochastic model; Stochastic models; chaotic signals; model equations; modeling; modeling and forecast; no
版次1
doihttps://doi.org/10.1007/978-3-642-12601-7
isbn_softcover978-3-642-26482-5
isbn_ebook978-3-642-12601-7Series ISSN 0172-7389 Series E-ISSN 2198-333X
issn_series 0172-7389
copyrightSpringer-Verlag Berlin Heidelberg 2010
The information of publication is updating

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Problem Posing in Modelling from Data Seriesd represent them with a scheme (Fig. 5.1). The procedure is started with consideration of available information about an object (including previously obtained experimental data from the object or similar ones, a theory developed for the class of objects under investigation and intuitive ideas) from
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Model Equations: “Black Box” Reconstructiontriguing thing is that a model capable of reproducing an observed behaviour or predicting further evolution should be obtained only from an observed time series, i.e. “from nothing” at first sight. Chances for a success are not large. Even more so, a “good” model would become a valuable tool to char
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Identification of Directional Couplingsocesses under study. The problem of . is encountered in multiple fields including physics (Bezruchko et al., 2003), geophysics (Maraun and Kurths, 2005; Mokhov and Smirnov, 2006, 2008; Mosedale et al., 2006; Palus and Novotna, 2006; Verdes, 2005; Wang et al., 2004), cardiology (Rosenblum et al., 200
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