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Marvin L. System Identification with MATLAB. Linear Models

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Marvin L. System Identification with MATLAB. Linear Models
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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