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Informationen zum Autor SERGIO BITTANTI is Emeritus Professor of Model Identification and Data Analysis (MIDA) at the Politecnico di Milano, Milan, Italy, where his intense activity of research and teaching has attracted the attention of many young researchers. He started teaching the course of MIDA years ago, with just a few students. Today the course is offered in various sections with about one thousand students. He has organized a number of workshops and conferences, and has served as member of the Program Committee of more than 70 international congresses. He has for many years been associated with the National Research Council (CNR) of Italy and is a member of the Academy of Science and Literature of Milan (Istituto Lombardo ? Accademia di Scienze e Lettere). He received many awards, in particular the title of Ambassador of the city of Milan and the medal of the President of the Italian Republic for the IFAC World Congress held in Milan in 2011 with a record number of attendees from 73 Countries. Website: http://home.deib.polimi.it/bittanti/ Klappentext 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 Zusammenfassung 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 Inhaltsverzeichnis Introduction xi Acknowledgments xv 1 Stationary Processes and Time Series 1 1.1 Introduction 1 1.2 The Prediction Problem 1 1.3 Random Variable 4 1.4 Random Vector 5 1.4.1 Covariance Coefficient 7 1.5 Stationary Process 9 1.6 White Process 11 1.7 MA Process 12 1.8 AR Process 16 1.8.1 Study of the AR(1) Process 16 1.9 Yule-Walker Equations 20 1.9.1 Yule-Walker Equations for the AR(1) Process 20 1.9.2 Yule-Walker Equations for the AR(2) and AR(n) Process 21 1.10 ARMA Process 23 1.11 Spectrum of a Stationary Process 24 1.11.1 Spectrum Properties 24 1.11.2 Spectral Diagram 25 1.11.3 Maximum Fr...
