User`s guide

2 Choosing Your System Identification Strategy
Supported Models for Time- and Frequency-Domain Data
In this section...
“Supported Models for Time-D o m ain Data” on page 2-4
“Supported Models for Frequency-Domain Data” on page 2-5
Supported Models for Time-Domain Data
Continuous-Time Models
You can directly estimate the following types of continuous-time models:
• Low-order transfer functions. See “Identifying Low-Order Transfer
Functions (Process Models)” on page 3-22.
• Input-output polynomial models. See “Identifying Input-Output Polynomial
Models” on p ag e 3-41.
• State-space models. See “Identifying State-Space Models” on page 3-73.
To get a linear, continuous-time model of arbitrary structure for tim e-do m ain
data, y ou can estimate a discrete-time model, and then use
d2c to transform
it to a continuous-time model.
Discrete-Time Models
You can estimate all linear and nonlinear models supported by the
System Identification Toolbox product as discrete -time models, except the
continuous-time transfer functions (process m odels ). For more information
about process models, see “Identifying Low-Order Transfer Functions (Process
Models)” on page 3-22.
ODEs (Grey-Box Models)
You can estimate both continuous-time and discrete-time models from
time-domain data for linear and nonlinear differential and difference
equations. See Chapter 5, “ODE Parameter Estimation (Grey-Box Modeling)”.
2-4