Preface; Contents; Introduction; Formulation of the Parameter Estimation Problem; Computation of Parameters in Linear Models -- Linear Regression; Gauss-Newton Method for Algebraic Models; Other Nonlinear Regression Methods for Algebraic Models; Gauss-Newton Method for Ordinary Differential Equation (ODE) Models'; Shortcut Estimation Methods for Ordinary Differential Equation (ODE) Models; Practical Guidelines for Algorithm Implementation; Constrained Parameter Estimation; Gauss-Newton Method for Partial Differential Equation (PDE) Models; Statistical Inferences; Design of Experiments
Summary
A presentation of important optimization methods used for parameter estimation. It focuses on the Gauss-Newton method for systems and processes represented by algebraic or differential equation models
Bibliography
Includes bibliographical references (pages 391-402) and index
