Requirements on Data Sampling :: Data Import and Processing (System Identification Toolbox™)

Contents

Index

• Getting Started

• Choosing Your System Identification Strategy

• Data Import and Processing

Types of Data You Can Model

Ways to Process Data for System Identification

Requirements on Data Sampling

• Importing Data into the MATLAB Workspace

• Importing Data into the GUI

• Representing Time- and Frequency-Domain Data Using iddata Objects

• Representing Frequency-Response Data Using idfrd Objects

• Analyzing Data Quality

• Selecting Subsets of Data

• Handling Missing Data and Outliers

• Handling Offsets and Trends in Data

• Resampling Data

• Filtering Data

• Generating Data Using Simulation

• Transforming Between Time- and Frequency-Domain Data

• Manipulating Complex-Valued Data

• Linear Model Identification

• Nonlinear Black-Box Model Identification

• ODE Parameter Estimation (Grey-Box Modeling)

• Time Series Model Identification

• Recursive Techniques for Model Identification

• Model Analysis

• Using Identified Models in Control Design

• Using System Identification Toolbox Blocks

• Using the System Identification Tool GUI

• Release Notes

Learn more about System Identification Toolbox

Requirements on Data Sampling

A sampling interval is the time between successive data samples.

The System Identification Tool GUI only supports uniformly sampled data.

The System Identification Toolbox product provides limited support for nonuniformly sampled data. For more information about specifying uniform and nonuniform time vectors, see Constructing an iddata Object for Time-Domain Data.

Ways to Process Data for System Identification

Ways to Process Data for System Identification

Importing Data into the MATLAB Workspace

Importing Data into the MATLAB Workspace

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