System Identification: Theory for the User

1. Introduction.

PART I. SYSTEMS AND MODELS. 2. Time-Invariant Linear Systems.
3. Simulation, Prediction, and Control.
4. Models of Linear Time-Invariant Systems.
5. Models for Time-Varying and Nonlinear Systems.

PART II. METHODS. 6. Nonparametric Time- and Frequency-Domain Methods.
7.Parameter Estimation Methods.
8.Covergence and Consistency.
9. Asymptotic Distribution of Parameter Estimates.
10. Computing the Estimate.
11. Recursive Estimation Methods.

PART III. USER'S CHOICES. 12. Options and Objectives.
13. Affecting the Bias Distribution of Transfer-Function Estimates.
14. Experiment Design.
15. Choice of Identification Criterion.
16. Model Structure Selection and Model Validation.
17. System Identification in Practice.
Appendix I. Some Concepts from Probability Theory.
Appendix II. Some Statistical Techniques for Linear Regressions.

Auteur

LENNART LJUNG is Professor of the Chair of Automatic Control in the Department of Electrical Engineering, Linksping University, Sweden. He is the author of nine books and over 100 articles in refereed international journals, as well as the author of the field's leading software package, System Identification Toolbox for MATLAB.

Texte du rabat

Appropriate for courses in System Identification. This book is a comprehensive and coherent description of the theory, methodology and practice of System Identification-the science of building mathematical models of dynamic systems by observing input/output data. It puts the user in focus, giving the necessary background to understand theoretical foundation and emphasizing the practical aspects of the options and choices that face the user. The Second Edition has been updated to include material on subspace methods, non-linear black box models-such as neural networks-and methods that use frequency domain data.

Résumé
Suitable for courses in System Identification, this book provides a description of the theory, methodology and practice of System Identification - the science of building mathematical models of dynamic systems by observing input/output data. It includes material on subspace methods, non-linear black box models - such as neural networks.

Contenu

1. Introduction.

PART I. SYSTEMS AND MODELS. 2. Time-Invariant Linear Systems.
3. Simulation, Prediction, and Control.
4. Models of Linear Time-Invariant Systems.
5. Models for Time-Varying and Nonlinear Systems.

PART II. METHODS. 6. Nonparametric Time- and Frequency-Domain Methods.
7.Parameter Estimation Methods.
8.Covergence and Consistency.
9. Asymptotic Distribution of Parameter Estimates.
10. Computing the Estimate.
11. Recursive Estimation Methods.

PART III. USER'S CHOICES. 12. Options and Objectives.
13. Affecting the Bias Distribution of Transfer-Function Estimates.
14. Experiment Design.
15. Choice of Identification Criterion.
16. Model Structure Selection and Model Validation.
17. System Identification in Practice.
Appendix I. Some Concepts from Probability Theory.
Appendix II. Some Statistical Techniques for Linear Regressions.