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.
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