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Extracting Knowledge from Time Series: An Introduction to Nonlinear Empirical Modeling - Couverture rigide
Synopsis
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.
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Présentation de l'éditeur
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.
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Extracting Knowledge From Time Series: An Introduction to Nonlinear Empirical Modeling (Springer Series in Synergetics)
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Springer, 2010
ISBN 10 : 3642126006
ISBN 13 : 9783642126000
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As New. UK stocked, available immediately. Hardcover, published by Springer in 2010. A superb copy throughout, in glossy illustrated covers. The contents are unmarked. Weight (unpacked) is 802 grams. N° de réf. du vendeur sh2xajsh802
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Extracting Knowledge From Time Series
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Springer Berlin Heidelberg, 2010
ISBN 10 : 3642126006
ISBN 13 : 9783642126000
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Couverture rigide
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Useful as a self-study guideGives a modern approach and practical examplesWritten by well known authors having made many contribution to the fieldMathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of. N° de réf. du vendeur 5049916
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Extracting Knowledge From Time Series : An Introduction to Nonlinear Empirical Modeling
Edité par
Springer Berlin Heidelberg, 2010
ISBN 10 : 3642126006
ISBN 13 : 9783642126000
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783642126000
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Extracting Knowledge From Time Series
Edité par
Springer Berlin Heidelberg Sep 2010, 2010
ISBN 10 : 3642126006
ISBN 13 : 9783642126000
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 432 pp. Englisch. N° de réf. du vendeur 9783642126000
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Extracting Knowledge From Time Series: An Introduction to Nonlinear Empirical Modeling (Springer Series in Synergetics)
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Extracting Knowledge From Time Series
Edité par
Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2010, 2010
ISBN 10 : 3642126006
ISBN 13 : 9783642126000
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 432 pp. Englisch. N° de réf. du vendeur 9783642126000
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Extracting Knowledge from Time Series : An Introduction to Nonlinear Empirical Modeling
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