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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 1Misfit 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.
