The presentation of a coherent methodology for the estimation of the parameters of mathematical models from experimental data is examined in this volume. Many topics are covered including the choice of the structure of the mathematical model, the choice of a performance criterion to compare models, the optimization of this performance criterion, the evaluation of the uncertainty in the estimated parameters, the design of experiments so as to get the most relevant data and the critical analysis of results. There are also several features unique to the work such as an up-to-date presentation of the methodology for testing models for identifiability and distinguishability and a comprehensive treatment of parametric optimization which includes greater consider ation of numerical aspects and which examines recursive and non-recursive methods for linear and nonlinear models.
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Text: English (translation)
Original Language: French
Original Language: French
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Key Phrases - Statistically Improbable Phrases (SIPs):
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posterior feasible set, prior feasible domain, prior feasible set, ellipsoidal algorithm, cost contours, optimal input sequence, adjoint code, successive search directions, outer ellipsoid, ellipsoid centred, regressor vector, optimal experiment, structural identifiability, expectation surface, direct code, rival structures, prediction discrepancy, adaptive random search, exponential forgetting, idealized framework, adjoint state, stochastic gradient algorithm, quadratic cost functions, correction gain, extended least squares Key Phrases - Capitalized Phrases (CAPs): (learn more)
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posterior feasible set, prior feasible domain, prior feasible set, ellipsoidal algorithm, cost contours, optimal input sequence, adjoint code, successive search directions, outer ellipsoid, ellipsoid centred, regressor vector, optimal experiment, structural identifiability, expectation surface, direct code, rival structures, prediction discrepancy, adaptive random search, exponential forgetting, idealized framework, adjoint state, stochastic gradient algorithm, quadratic cost functions, correction gain, extended least squares Key Phrases - Capitalized Phrases (CAPs): (learn more)
Data Set Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 91
books:
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- Data Analysis (New Topics for Secondary School Mathematics) by Nc School of Sci and Math on 6 pages
- Optimal Design and Analysis of Experiments by Y. Dodge on 4 pages
- Identifiability of Parametric Models by Eric Walter in Back Matter (1), Back Matter (2), and Back Matter (3)
- System Identification Parameter and State Estimation by P. Eykhoff in Back Matter (1), Back Matter (2), and Back Matter (3)
- Bounding Approaches to System Identification by M. Milanese in Back Matter (1), and Back Matter (2)
See all 91 books this book cites
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