Name: Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (en Inglés)
SKU: 22950896
Price: 326.91 USD
Availability: InStock
Author: Schittkowski, Klaus
ISBN: 9781402010798

portada Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (en Inglés)

Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (en Inglés)

Formato

Libro Físico

Autor

Klaus Schittkowski

Editorial

Springer

Año

2002

Idioma

Inglés

N° páginas

396

Encuadernación

Tapa Dura

Dimensiones

24.4x16.6x2.3 cm

Peso

0.87 kg.

ISBN

1402010796

ISBN13

9781402010798

Categorías

Matemáticas aplicadas
Análisis y diseño de sistemas

Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (en Inglés)

Klaus Schittkowski (Autor) · Springer · Tapa Dura

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Reseña del libro "Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software (en Inglés)"

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.