No products
View larger
Principles of System IdentificationTheory and Practice By Arun K. Tangirala
M00001997
New product
In stock
More info
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/c397Table of Contents
PART I INTRODUCTION TO IDENTIFICATION AND MODELS FOR LINEAR DETERMINISTIC SYSTEMS
Introduction
Motivation
Historical developments
System Identification
Systematic identification
Flow of learning material
Software
A Journey into Identification
Identifiability
Signal-to-Noise ratio
Overfitting
A modeling example: liquid level system
Reflections and summary
Mathematical Descriptions of Processes: Models
Definition of a model
Classification of models
Models for Discrete-Time LTI Systems
Convolution model
Response models
Difference equation form
State-space descriptions
Illustrative example in MATLAB: estimating LTI models
Summary
Transform-Domain Models for Linear Time-Invariant Systems
Frequency response function
Transfer function form
Empirical transfer function (ETF)
Closure
Sampling and Discretization
Discretization
Sampling
Summary
PART II MODELS FOR RANDOM PROCESSES
Random Processes
Introductory remarks
Random variables and probability
Probability theory
Statistical properties of random variables
Random signals and processes
Time-series analysis
Summary
Time-Domain Analysis: Correlation Functions
Motivation
Auto-covariance function
White-noise process
Cross-covariance function
Partial correlation functions
Summary
Models for Linear Stationary Processes
Motivation
Basic ideas
Linear stationary processes
Moving average models
Auto-regressive models
Auto-regressive moving average models
Auto-regressive integrated moving average models
Summary
Fourier Analysis and Spectral Analysis of Deterministic Signals
Motivation
Definitions
Fourier representations of deterministic processes
Discrete Fourier Transform (DFT)
Summary
Spectral Representations of Random Processes
Introduction
Power spectral density of a random process
Spectral characteristics of standard processes
Cross-spectral density and coherence
Partial coherence
Spectral factorization
Summary
PART III ESTIMATION METHODS
Introduction to Estimation
Motivation
A simple example: constant embedded in noise
Definitions and terminology
Types of estimation problems
Estimation methods
Historical notes
Goodness of Estimators
Introduction
Fisher information
Bias
Variance
Efficiency
Sufficiency
Cramer-Rao’s inequality
Asymptotic bias
Mean square error
Consistency
Distribution of estimates
Hypothesis testing and confidence intervals
Empirical methods for hypothesis testing
Summary
Appendix
Estimation Methods: Part I
Introduction
Method of moments estimators
Least squares estimators
Non-linear least squares
Summary
Appendix
Estimation Methods: Part II
Maximum likelihood estimators
Bayesian estimators
Summary
Estimation of Signal Properties
Introduction
Estimation of mean and variance
Estimators of correlation
Estimation of correlation functions
Estimation of auto-power Spectra
Estimation of cross-spectral density
Estimation of coherence
Summary
PART IV IDENTIFICATION OF DYNAMIC MODELS - CONCEPTS AND PRINCIPLES
Non-Parametric and Parametric Models for Identification
Introduction
The overall model
Quasi-stationarity
Non-parametric descriptions
Parametric descriptions
Summary
Predictions
Introduction
Conditional expectation and linear predictors
One-step ahead prediction and innovations
Multi-step and infinite-step ahead predictions
Predictor model: An alternative LTI description
Identifiability
Summary
Identification of Parametric Time-Series Models
Introduction
Estimation of AR models
Estimation of MA models
Estimation of ARMA models
Summary
Identification of Non-Parametric Input-Output Models
Recap
Impulse response estimation
Step response estimation
Estimation of frequency response function
Estimating the disturbance spectrum
Summary
Identification of Parametric Input-Output Models
Recap
Prediction-error minimization (PEM) methods
Properties of the PEM estimator
Variance and distribution of PEM-QC estimators
Accuracy of parametrized FRF estimates using PEM
Algorithms for estimating specific parametric models
Correlation methods
Summary
Statistical and Practical Elements of Model Building
Introduction
Informative Data
Input design for identification
Data pre-processing
Time-delay estimation
Model development
Summary
Identification of State-Space Models
Introduction
Mathematical essentials and basic ideas
Kalman filter
Foundations for subspace identification
Preliminaries for subspace identification methods
Subspace identification algorithms
Structured state-space models
Summary
Case Studies
ARIMA model of industrial dryer temperature
Simulated process: developing an input-output model
Process with random walk noise
Multivariable modeling of a four-tank system
Summary
PART V ADVANCED CONCEPTS
Advanced Topics in SISO Identification
Identification of linear time-varying systems
Non-linear identification
Closed-loop identification
Summary
Linear Multivariable Identification
Motivation
Estimation of time delays in MIMO systems
Principal component analysis (PCA)
Summary
References
Index
View More
Author(s)
Biography
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.
Reviews
"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
Related Titles
