This book determines adjustable parameters in mathematical models that describe steady state or dynamic systems, presenting the most important optimization methods used for parameter estimation. It focuses on the Gauss-Newton method and its modifications for systems and processes represented by algebraic or differential equation models.

chapter |6 pages


chapter |18 pages

Gauss-Newton Method for Algebraic Models

chapter |9 pages

Constrained Parameter Estimation

chapter |8 pages

Statistical Inferences

chapter |33 pages

Design of Experiments

chapter |8 pages

Recursive Parameter Estimation