Marvin L. SYSTEM IDENTIFICATION with MATLAB. Linear Models. - 2016 - 267p. 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 - 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
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Marvin L. System Identification with MATLAB. Non Linear Models, ODEs and Time Series. - 2016. - 207p.
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...