Model Identification and Data Analysis - Hardcover

Über die Autorin bzw. den 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/

Von der hinteren Coverseite

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

Aus dem 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