Identification of Parametric Models from Experimental Data

E. Walter (SUPELEC, France)
and L. Pronzato (CNRS, Sophia-Antipolis, France)

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 consideration of numerical aspects and which examines recursive and non-recursive methods for linear and nonlinear models.

Springer-Verlag, Berlin-Heidelberg

ISBN 3-540-76119-5, XVIII, 413 pp. 1997

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