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Extracting Knowledge From Time Series: An Introduction to by Boris P. Bezruchko,Dmitry A. Smirnov PDF

By Boris P. Bezruchko,Dmitry A. Smirnov

Mathematical modelling is ubiquitous. nearly each e-book in distinctive technological know-how touches on mathematical types of a definite classification of phenomena, on kind of speci?c ways to development and research of types, on their functions, and so forth. As many textbooks with comparable titles, half I of our ebook is dedicated to basic qu- tions of modelling. half II re?ects our expert pursuits as physicists who spent a lot time to investigations within the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter course of analysis is understood for a very long time as “system identi?cation” within the framework of mathematical data and automated keep watch over thought. It has its roots within the challenge of approximating experimental information issues on a aircraft with a soft curve. presently, researchers objective on the description of advanced behaviour (irregular, chaotic, non-stationary and noise-corrupted indications that are common of real-world items and phenomena) with fairly easy non-linear differential or distinction version equations instead of with bulky particular features of time. within the moment 1/2 the 20th century, it has develop into transparent that such equations of a s- ?ciently low order can convey non-trivial ideas that promise suf?ciently easy modelling of complicated procedures; in keeping with the innovations of non-linear dynamics, chaotic regimes might be confirmed already via a third-order non-linear traditional differential equation, whereas complicated behaviour in a linear version might be caused both by way of random in?uence (noise) or via a truly excessive order of equations.

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Extracting Knowledge From Time Series: An Introduction to Nonlinear Empirical Modeling (Springer Series in Synergetics) by Boris P. Bezruchko,Dmitry A. Smirnov


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