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System Identification: Theory for the User, 2nd edition

Published by Prentice Hall PTR (December 29, 1998) © 1997

  • Lennart Ljung Linkoping University, Sweden

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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.

  • Implements all methods in the System Identification Toolbox (to be run with MATLAB). Pg.___
  • Serves as a complete update to what has been the leading book on the market, as well as the most cited one, for the past decade. It has been translated into Russian and Chinese. Pg.___
  • Integrates a wealth of problem sets to both reinforce and challenge readers' understanding of key concepts. Pg.___
  • Links coverage to the System Identification Toolbox, the internationally best selling software for System Identification. Pg.___


 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.

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