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Full Description
Exact and Approximate Modeling of Linear Systems elegantly introduces the behavioural approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations. The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular purpose.
This book presents exact subspace-type and approximate optimization-based identification methods, as well as representation-free problem formulations, an overview of solution approaches, and software implementation. Readers will find an exposition of a wide variety of modeling problems starting from observed data. The presented theory leads to algorithms that are implemented in C language and in MATLAB.
Contents
Preface
Chapter 1: Introduction
Chapter 2: Approximate Modeling via Misfit Minimization
Part I: Static Problems. Chapter 3: Weighted Total Least Squares
Chapter 4: Structured Total Least Squares
Chapter 5: Bilinear Errors-in-Variables Model
Chapter 6: Ellipsoid Fitting
Part II: Dynamic Problems. Chapter 7: Introduction to Dynamical Models
Chapter 8: Exact Identification
Chapter 9: Balanced Model Identification
Chapter 10: Errors-in-Variables Smoothing and Filtering
Chapter 11: Approximate System Identification
Chapter 12: Conclusions
Appendix A: Proofs
Appendix B: Software
Notation
Bibliography
Index.



