发帖
雷达卡
by Marvin L.
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”
本帖隐藏的内容
SYSTEM IDENTIFICATION with MATLAB. Linear Models.pdf
(2.79 MB, 需要: 10 个论坛币)
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