User`s guide

Supported Models for Time- and Frequency-Domain Data
Nonlinear Models
You can estimate discrete-time H amm erstein-Wiener and nonlinear ARX
models from time-domain data. See Chapter 4, “Nonlinear Black-Box Model
Identification”.
You can also estimate nonlinear grey-box models from time-do main data. See
“Estimating Nonlin e ar Grey-Bo x Models” on page 5-16.
Supported Models for F requency-Domain Data
There are two types of frequency-domain data:
• Continuous-time data
• Discrete-time data
You specify frequency-domain data as continuous- or discrete-time when you
either import data into the System Identification Tool GU I or create a Sy stem
Identification Toolbox data object. For more information about representing
your data as System Identification Toolbox d ata objects, see Chapter 1, “Data
Processing”.
To designate discrete-time data, you set the sampling interval of the data to
the experimental data sampling interval. To designate continuous-time data,
you must set the sampling interval of the data to zero. Setting the sampling
interval to zero corresponds to taking a Fourier transform of continuous-time
data.
Continuous-Time Models
You can estimate the following types of continuous-time m odels directly:
• 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 .
From continuous-tim e frequency-d omain data, yo u can es timate
continuous-time state-space models. From discrete-time frequency-domain
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