Master Techniques and Successfully Build Models Using a Single Resource
Vital to all data-driven or measurement-based process operations, system identification is an interface that is based on observational science, and centers on developing mathematical models from observed data. Principles of System Identification: Theory and Practice is an introductory-level book that presents the basic foundations and underlying methods relevant to system identification. The overall scope of the book focuses on system identification with an emphasis on practice, and concentrates most specifically on discrete-time linear system identification.
Useful for Both Theory and Practice
The book presents the foundational pillars of identification, namely, the theory of discrete-time LTI systems, the basics of signal processing, the theory of random processes, and estimation theory. It explains the core theoretical concepts of building (linear) dynamic models from experimental data, as well as the experimental and practical aspects of identification. The author offers glimpses of modern developments in this area, and provides numerical and simulation-based examples, case studies, end-of-chapter problems, and other ample references to code for illustration and training.
Comprising 26 chapters, and ideal for coursework and self-study, this extensive text:
- Provides the essential concepts of identification
- Lays down the foundations of mathematical descriptions of systems, random processes, and estimation in the context of identification
- Discusses the theory pertaining to non-parametric and parametric models for deterministic-plus-stochastic LTI systems in detail
- Demonstrates the concepts and methods of identification on different case-studies
- Presents a gradual development of state-space identification and grey-box modeling
- Offers an overview of advanced topics of identification namely the linear time-varying (LTV), non-linear, and closed-loop identification
- Discusses a multivariable approach to identification using the iterative principal component analysis
- Embeds MATLAB® codes for illustrated examples in the text at the respective points
Principles of System Identification: Theory and Practice
presents a formal base in LTI deterministic and stochastic systems modeling and estimation theory; it is a one-stop reference for introductory to moderately advanced courses on system identification, as well as introductory courses on stochastic signal processing or time-series analysis.The MATLAB scripts and SIMULINK models used as examples and case studies in the book are also available on the author's website: http://arunkt.wix.com/homepage#!textbook/c397
Table of Contents:
PART I INTRODUCTION TO IDENTIFICATION AND MODELS FOR LINEAR DETERMINISTIC SYSTEMS. Introduction. A Journey into Identification. Mathematical Descriptions of Processes: Models. Models for Discrete-Time LTI Systems. Transform-Domain Models for Linear TIme-Invariant Systems. Sampling and Discretization. PART II MODELS FOR RANDOM PROCESSES. Random Processes. Time-Domain Analysis: Correlation Functions. Models for Linear Stationary Processes. Fourier Analysis and Spectral Analysis of Deterministic Signals. Spectral Representations of Random Processes. PART III ESTIMATION METHODS. Introduction to Estimation. Goodness of Estimators. Estimation Methods: Part I. Estimation Methods: Part II. Estimation of Signal Properties. PART IV IDENTIFICATION OF DYNAMIC MODELS - CONCEPTS AND. PRINCIPLES. Non-Parametric and Parametric Models for Identification. Predictions. Identification of Parametric Time-Series Models. Identification of Non-Parametric Input-Output Models. Identification of Parametric Input-Output Models. Statistical and Practical Elements of Model Building. Identification of State-Space Models. Case Studies. PART V ADVANCED CONCEPTS. Advanced Topics in SISO Identification. Linear Multivariable Identification. References. Index.
About the Author :
Arun K. Tangirala an Associate Professor at the Department of Chemical Engineering, IIT Madras, India. He obtained his B. Tech. (Chemical Engineering) from IIT Madras, India and Ph.D. (Process Control & Monitoring) from the University of Alberta, Canada in the years 1996 and 2001, respectively. Dr. Tangirala specializes in process control, modelling, monitoring and multivariate data analysis. His research group is focused on solving some of the cutting edge problems in data-driven analysis and modelling. A recipient of different teaching and research awards, he has conducted several workshops and short-term courses on data analysis and process identification.
Review :
"This book is an encyclopedia of linear system identification. … A practicing engineer’s perfect guide to system identification and its applications."
—Bhushan Gopaluni, University of British Columbia, Vancouver, Canada
"Very good framework. … It reflects the core idea and dominant methods in this field."
—Fan Yang, Department of Automation, Tsinghua University, Beijing, China
"Students these days are looking to become knowledgeable about advanced topics as quickly and efficiently as possible, and therefore want to find that one-stop reference or course to bring them up to speed. This book is a welcome addition to the literature for students and teachers alike [who are] interested in doing just that in the field of system identification."
—William R. Cluett, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontairo, Canada
"… nicely goes over all the key principles and concepts in way that is accessible to the average reader, yet touches upon the subtleties of the theoretical foundations. This book has the qualities to be an attractive entry point for anyone interested in this subject. In fact, the book is written in a way that it will draw the reader in with its simple and systematic exposition of this interesting and useful subject."
—Harish Palanthandalam-Madapusi, Indian Institute of Technology Gandhinagar, Ahmedabad
"The author is to be congratulated for writing this extensive textbook. It builds on the shoulders of the giants in the field like George Box and Ljung and provides the reader with an up-to-date, encyclopedic-like travelogue through the theory and practice of system identification. The author provides relatively simple examples in different places throughout the book to help the reader appreciate the problem without getting distracted by too much complexity. The majority of examples are accompanied by Matlab code to enable the reader to easily run simulations on his or her own and duplicate the author’s results. At the end of each chapter, the reader will find review questions to provoke reflection on what has just been read as well as exercises to gain practice and build confidence."
—IEEE Control Systems Magazine, April 2017 Issue
