dynamical systems introduction applications (42 resultados)

Softcover. Condición: Bon. Ancien livre de bibliothèque. Traces d'usure sur la couverture. Edition 1994. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Signs of wear on the cover. Edition 1994. Ammareal gives back up to 15% of this item's net price to charity organizations.

Broschiert. Condición: Gut. 252 Seiten Das hier angebotene Buch stammt aus einer teilaufgelösten Bibliothek und kann die entsprechenden Kennzeichnungen aufweisen (Rückenschild, Instituts-Stempel.); der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 455.

Paperback. Condición: Very Good. No Jacket. Paperback, 252 pp. Light edge wear, else text clean. Book.

Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 58 ABR 9780805301021 Sprache: Englisch Gewicht in Gramm: 1330.

Condición: New. In.

Hardcover. Condición: Good. No Jacket. Missing dust jacket; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.01.

hardcover. Condición: Good. Good. Lightly used, may have light reading wear. NO markings in text. Pasadena's finest new and used bookstore since 1992.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - For many applications, ranging from controls engineering to natural sciences and economics, precise dynamic models must be derived. In the vast majority of applications, such precise models cannot be derived by theoretical considerations only. The book discusses methods, which allow the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification respectively.After a short introduction into the required methodology of continuous-time and discrete-time linear systems, the focus is first on the identification of non-parametric models with continuous-time signals employing methods such as Fourier transform, measurement of the frequency response and correlation analysis. Then, the parameter estimation for parametric models is presented with a focus on the method of Least Squares, followed by some of its most prominent modifications. Issues such as parameter estimation for time-variant processes, parameter estimation in closed-loop, parameter estimation for differential equations, continuous time processes and efficient implementations of the algorithms are discussed. The different methods are compared and an outlook is given on non-linear system identification methods, such as neural networks and look-up tables.Real experimental data are given on CD-ROM, allowing to test many of the methods presented in the book with real measurements. Powerpoint slides for a 12-14 week graduate level course can be made available to teachers.

Condición: New. In.

Hardcover, 296 pages. Condición: Good. First thus, no dust jacket. Minor wear and soiling to beige cloth boards, corners a little frayed. Head and foot of spine lightly bumped. Minor soiling and foxing to text block edges. Binding cracked at title page and at page 296, pages remain secure. Pages appear free of writing, highlighting, and soil.

Condición: New. In.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Real life phenomena in engineering, natural, or medical sciences are often described by a mathematical model with the goal to analyze numerically the behaviour of the system. Advantages of mathematical models are their cheap availability, the possibility of studying extreme situations that cannot be handled by experiments, or of simulating real systems during the design phase before constructing a first prototype. Moreover, they serve to verify decisions, to avoid expensive and time consuming experimental tests, to analyze, understand, and explain the behaviour of systems, or to optimize design and production. As soon as a mathematical model contains differential dependencies from an additional parameter, typically the time, we call it a dynamical model. There are two key questions always arising in a practical environment: 1 Is the mathematical model correct 2 How can I quantify model parameters that cannot be measured directly In principle, both questions are easily answered as soon as some experimental data are available. The idea is to compare measured data with predicted model function values and to minimize the differences over the whole parameter space. We have to reject a model if we are unable to find a reasonably accurate fit. To summarize, parameter estimation or data fitting, respectively, is extremely important in all practical situations, where a mathematical model and corresponding experimental data are available to describe the behaviour of a dynamical system.