extracting knowledge time series (29 résultats)

Etat : New.

Etat : New.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

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.

Etat : New. In.

Etat : New. In.

PF. Etat : New.

Etat : New.

Etat : New.

Etat : New. pp. 432.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Paperback. Etat : Brand New. 2010 edition. 432 pages. 9.00x5.90x1.20 inches. In Stock.

Hardcover. Etat : Brand New. 21 pages. 9.25x6.25x0.25 inches. In Stock.

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.

Taschenbuch. 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.

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.

Taschenbuch. Etat : Neu. Extracting Knowledge From Time Series | An Introduction to Nonlinear Empirical Modeling | Dmitry A. Smirnov (u. a.) | Taschenbuch | xxii | Englisch | 2012 | Springer-Verlag GmbH | EAN 9783642264825 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. 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.

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.

Etat : New. Print on Demand pp. 432 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Etat : New. PRINT ON DEMAND pp. 432.

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.

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.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. 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.

Buch. Etat : Neu. Extracting Knowledge From Time Series | An Introduction to Nonlinear Empirical Modeling | Dmitry A. Smirnov (u. a.) | Buch | xxii | Englisch | 2010 | Springer-Verlag GmbH | EAN 9783642126000 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.