Model identification and data analysis

This book is about constructing models from experimental data. It covers a range of topics, from statistical data prediction to Kalman filtering, from black-box model identification to parameter estimation, from spectral analysis to predictive control. Written for graduate students, this textbook offers an approach that has proven successful throughout the many years during which its author has taught these topics at his University. The book : -Contains accessible methods explained step-by-step in simple terms -Offers an essential tool useful in a variety of fields, especially engineering, statistics, and mathematics -Includes an overview on random variables and stationary processes, as well as an introduction to discrete time models and matrix analysis -Incorporates historical commentaries to put into perspective the developments that have brought the discipline to its current state -Provides many examples and solved problems to complement the presentation and facilitate comprehension of the techniques presented

This book is about constructing models from experimental data. It covers a range of topics, from statistical data prediction to Kalman filtering, from black-box model identification to parameter estimation, from spectral analysis to predictive control.

Written for graduate students, this textbook offers an approach that has proven successful throughout the many years during which its author has taught these topics at his University.

The book:

• Contains accessible methods explained step-by-step in simple terms
• Offers an essential tool useful in a variety of fields, especially engineering, statistics, and mathematics
• Includes an overview on random variables and stationary processes, as well as an introduction to discrete time models and matrix analysis
• Incorporates historical commentaries to put into perspective the developments that have brought the discipline to its current state
• Provides many examples and solved problems to complement the presentation and facilitate comprehension of the techniques presented

Cover......Page 1 Model Identification and Data Analysis......Page 3 © 2019......Page 4 Contents......Page 5 Introduction......Page 11 Acknowledgments......Page 14 1 Stationary Processes and Time Series......Page 16 2 Estimation of Process Characteristics......Page 62 3 Prediction......Page 75 4 Model Identification......Page 94 5 Identification of Input–Output Models......Page 119 6 Model Complexity Selection......Page 167 7 Identification of State Space Models......Page 184 8 Predictive Control......Page 197 9 Kalman Filtering and Prediction......Page 219 10 Parameter Identification in a Given Model......Page 290 11 Case Studies......Page 299 Appendix A.Linear Dynamical Systems......Page 317 Appendix B.Matrices......Page 338 Appendix C.Problems and Solutions......Page 363 Bibliography......Page 396 Index......Page 401