Book description
This book is about constructing models from experimental data. It covers a range of topics, from statistical data prediction to Kalman filtering, from blackbox 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 stepbystep 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
Table of contents
 Cover
 Introduction
 Acknowledgments

1 Stationary Processes and Time Series
 1.1 Introduction
 1.2 The Prediction Problem
 1.3 Random Variable
 1.4 Random Vector
 1.5 Stationary Process
 1.6 White Process
 1.7 MA Process
 1.8 AR Process
 1.9 Yule–Walker Equations
 1.10 ARMA Process
 1.11 Spectrum of a Stationary Process
 1.12 ARMA Model: Stability Test and Variance Computation
 1.13 Fundamental Theorem of Spectral Analysis
 1.14 Spectrum Drawing
 1.15 Proof of the Fundamental Theorem of Spectral Analysis
 1.16 Representations of a Stationary Process
 2 Estimation of Process Characteristics
 3 Prediction
 4 Model Identification
 5 Identification of Input–Output Models
 6 Model Complexity Selection
 7 Identification of State Space Models
 8 Predictive Control

9 Kalman Filtering and Prediction
 9.1 Introduction
 9.2 Kalman Approach to Prediction and Filtering Problems
 9.3 The Bayes Estimation Problem
 9.4 One‐step‐ahead Kalman Predictor
 9.5 Multistep Optimal Predictor
 9.6 Optimal Filter
 9.7 Steady‐State Predictor
 9.8 Innovation Representation
 9.9 Innovation Representation Versus Canonical Representation
 9.10 K‐Theory Versus K–W Theory
 9.11 Extended Kalman Filter – EKF
 9.12 The Robust Approach to Filtering
 10 Parameter Identification in a Given Model
 11 Case Studies
 Appendix A: Linear Dynamical Systems

Appendix B: Matrices
 B.1 Basics
 B.2 Eigenvalues
 B.3 Determinant and Inverse
 B.4 Rank
 B.5 Annihilating Polynomial
 B.6 Algebraic and Geometric Multiplicity
 B.7 Range and Null Space
 B.8 Quadratic Forms
 B.9 Derivative of a Scalar Function with Respect to a Vector
 B.10 Matrix Diagonalization via Similarity
 B.11 Matrix Diagonalization via Singular Value Decomposition
 B.12 Matrix Norm and Condition Number
 Appendix C: Problems and Solutions
 Bibliography
 Index
 End User License Agreement
Product information
 Title: Model Identification and Data Analysis
 Author(s):
 Release date: April 2019
 Publisher(s): Wiley
 ISBN: 9781119546368
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