User`s guide

Supported Continuous-Time and Disc rete-Time Models
Supported Continuous-Time and Discrete-Time Models
For linear and nonlinear ODEs (grey -box models), you ca n specify any
ordinary differential or difference equation to represent y our continuous-time
or discrete-time model in state-space form, respectively. In the linear case,
both time-domain and frequency-domain data are supported. In the nonlinear
case, only time-domain data is supported.
For black-box models , the follow i ng table s summ arize supported
continuous-time a nd discrete-time models.
Supported Continuous-Time Models
Model Type
Description
Low-order transfer functions
(process models)
Estimate low-order process models for up to three free poles
from either time- or frequency -domain data.
Linear input-output polynomial
models
To get a linear, continuous-time model of arbitrary
structure from time-domain data, you can estimate a
discrete-time model, and then use
d2c to transform it into a
continuous-time model.
For frequency-domain data, you can directly estimate only
the A RX and output-error (OE) continuous-time polynomial
models by setting the sam pling interval of the data to
0.
Other s tructures include noise models and are not supported
for frequency-domain data.
State-space models To get a linear, continuous-time m odel of arbitrary
structure for time-domain data, you can estimate a
discrete-time model, and then use
d2c to transform it into a
continuous-time model.
For frequency-domain data, you can estimate
continuous-time state-space models directly.
Linear ODEs (grey-box models Estimate o rdinar y differential equations (O DE s) from either
time- or frequency-domain data.
Nonlinear ODEs (grey-box)
models
Estimate arbitrary differential equations (ODEs) from
time-domain data.
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