Intro; Preface; Contents; Abbreviations; Notation; Notational Conventions; Summary of Assumptions; Assumptions on the System; Assumptions on the Noise; Assumptions on the Noise-free Input; Assumptions on the Experimental Conditions; Applicability; 1 Introduction; 1.1 Four Motivating Examples; 1.2 Outline of the Book; 1.3 Some Important Concepts in System Identification; 1.4 Some Notations; 1.5 Extensions and Bibliographical Notes; 2 The Static Case; 2.1 Line Fitting; 2.1.1 Some System Theoretic Considerations of Identifiability; 2.2 Confirmatory Factor Analysis; 2.2.1 The Modeling Part

2.2.2 Estimation Part2.3 The Frisch Scheme; 2.4 Extensions and Bibliographical Notes; 2.A Further Details; 2.A.1 Further Results for Line Fitting; 2.A.2 Consistency of the CFA Estimate; 3 The Errors-in-Variables Problem for Dynamic Systems; 3.1 The EIV Problem; 3.2 About Numerical Examples; 3.3 Two Special Cases; 3.4 Some Naïve Approaches; 3.4.1 Neglecting the Input Noise; 3.4.2 Estimating the Noise-Free Input Signal; 3.4.3 Rewriting the Model into Standard Form; 3.5 Extensions and Bibliographical Notes; 4 Identifiability Aspects; 4.1 Some General Aspects

4.2 Identifiability Analysis for Parametric Models4.3 Identifiability When Using Multiple Experiments; 4.4 Closed-Loop Operation; 4.5 Extensions and Bibliographical Notes; 5 Modeling Aspects; 5.1 Problem Statement and Notations; 5.2 Using Models with an Arbitrary Delay; 5.3 Continuous-Time EIV Models and Conversion to Discrete-Time; 5.4 Modeling the Noise Properties; 5.5 Frequency Domain Models; 5.6 Modeling the Total System; 5.7 Models for Multivariable Systems; 5.8 Classification of Estimators Based on Data Compression; 5.9 Model Order Determination; 5.9.1 Introduction

5.9.2 Some Approaches5.9.3 About the Rank Tests; 5.9.4 Discussion; 5.10 Extensions and Bibliographical Notes; 5.A Further Details; 5.A.1 Discrete-Time Model Approximation; 5.A.2 Analyzing Effects of Small Singular Values; 6 Elementary Methods; 6.1 The Least Squares Method; 6.2 The Instrumental Variable Method; 6.2.1 Description; 6.2.2 Consistency Analysis; 6.2.3 User Choices. Examples of Instrumental Vectors; 6.2.4 Instrumental Variable Methods Exploiting Higher-Order Statistics; 6.2.5 Other Instrumental Variable Techniques; 6.3 Extensions and Bibliographical Notes

7 Methods Based on Bias-Compensation7.1 The Basic Idea of Bias-Compensation; 7.2 The Bias-Eliminating Least Squares Method; 7.2.1 Introduction; 7.2.2 White Output Noise; 7.2.3 Correlated Output Noise; 7.3 The Frisch Scheme; 7.3.1 General Aspects; 7.3.2 White Output Noise; 7.3.3 Correlated Output Noise; 7.3.4 Using an Alternating Projection Algorithm; 7.4 The Generalized Instrumental Variable Method; 7.4.1 General Framework; 7.4.2 Various Examples; 7.4.3 GIVE Identification of MIMO Models; 7.5 Extensions and Bibliographical Notes; 7.5.1 BELS; 7.5.2 The Frisch Scheme; 7.5.3 GIVE