| Choosing Your System Identification Strategy | Creating, manipulating, estimating, and refining models
at the command line. |
| Data Import and Processing | Requirements on time-domain, frequency-domain, and
frequency-response data, and how to import data into the GUI, create
data objects at the command line, plot data, preprocess data, and
transform data. |
| Linear Model Identification | Describes how to estimate linear parametric models,
including low-order process models, polynomial models, and state-space
models |
| Nonlinear Black-Box Model Identification | Describes how to estimate nonlinear ARX and Hammerstein-Wiener
models for time-domain data. |
| ODE Parameter Estimation (Grey-Box Modeling) | Describes how to estimate linear and nonlinear grey-box
models, represented by ordinary differential equations or by discrete-time
difference equations. |
| Time Series Model Identification | Describes how to estimate linear and nonlinear models
for no-input and single- or multiple-output data. |
| Recursive Techniques for Model Identification | Introduces the use of recursive algorithms and segmentation
for modeling data. |
| Model Analysis | Describes how to plot, analyze, and validate linear
and nonlinear models. |
| Using Identified Models in Control Design | Describes how to use identified models in control
design applications with other MathWorks products. |
| Using System Identification Toolbox Blocks | Describes the System Identification Toolbox Library
blocks and provides references for more information. |
| Using the System Identification Tool GUI | How to start and manage GUI sessions, customize GUI
preferences, and manage data sets and models. |
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