ABSTRACT

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

Introduction

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