System Identification With Matlab Linear Models
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System Identification with MATLAB. Linear Models
Author: Marvin L.
language: en
Publisher: Createspace Independent Publishing Platform
Release Date: 2016-10-23
In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. The toolbox provides several linear and nonlinear black-box model structures, which have traditionally been useful for representing dynamic systems. This book develops the next tasks with linear models:* "Black-Box Modeling" * "Identifying Frequency-Response Models" * "Identifying Impulse-Response Models" * "Identifying Process Models" * "Identifying Input-Output Polynomial Models" * "Identifying State-Space Models" * "Identifying Transfer Function Models" * "Refining Linear Parametric Models"* "Refine ARMAX Model with Initial Parameter Guesses at Command Line"* "Refine Initial ARMAX Model at Command Line" * "Extracting Numerical Model Data" * "Transforming Between Discrete-Time and Continuous-Time Representations" * "Continuous-Discrete Conversion Methods" * "Effect of Input Intersample Behavior on Continuous-Time Models" * "Transforming Between Linear Model Representations" * "Subreferencing Models"* "Concatenating Models" * "Merging Models"* "Building and Estimating Process Models Using System Identification Toolbox* "Determining Model Order and Delay" 5* "Model Structure Selection: Determining Model Order and Input Delay" * "Frequency Domain Identification: Estimating Models Using Frequency Domain Data" * "Building Structured and User-Defined Models Using System Identification Toolbox"
Mastering System Identification in 100 Exercises
This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each. Each chapter features MATLAB exercises, discussions of the exercises, accompanying MATLAB downloads, and larger projects that serve as potential assignments in this learn-by-doing resource.
Principles of System Identification
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-serie...