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Titlebook: Numerical Data Fitting in Dynamical Systems; A Practical Introduc Klaus Schittkowski Book 2002 Springer Science+Business Media Dordrecht 20

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書(shū)目名稱(chēng)Numerical Data Fitting in Dynamical Systems
副標(biāo)題A Practical Introduc
編輯Klaus Schittkowski
視頻videohttp://file.papertrans.cn/669/668979/668979.mp4
叢書(shū)名稱(chēng)Applied Optimization
圖書(shū)封面Titlebook: Numerical Data Fitting in Dynamical Systems; A Practical Introduc Klaus Schittkowski Book 2002 Springer Science+Business Media Dordrecht 20
描述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
出版日期Book 2002
關(guān)鍵詞Algebra; Fitting; Hardware; Mathematica; dynamical systems; dynamische Systeme; linear optimization; model;
版次1
doihttps://doi.org/10.1007/978-1-4419-5762-7
isbn_softcover978-1-4757-6050-7
isbn_ebook978-1-4419-5762-7Series ISSN 1384-6485
issn_series 1384-6485
copyrightSpringer Science+Business Media Dordrecht 2002
The information of publication is updating

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Introduction,ally predicted values of a model function at certain time values. Thus, model parameters that cannot be measured directly can be identified by a least squares fit and analyzed subsequently in a quantitative way.
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Introduction,rs .., ..., .. of a mathematical model that describes a real life situation, by minimizing the distance of some known experimental data from theoretically predicted values of a model function at certain time values. Thus, model parameters that cannot be measured directly can be identified by a least
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Case Studies,te unknown parameters of a mathematical model that describes a realistic dynamical process. The examples represent typical application problems in biochemistry, chemistry, pharmaceutics and electrical, mechanical, or chemical engineering.
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