Author Description

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"