Description
Book SynopsisMaster 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 b
Trade 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
Table of ContentsPART 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.
