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Supported Models for Time-Domain Data |

You can directly estimate the following types of continuous-time models:

Transfer function models. See Identifying Transfer Function Models.

Process models. See Identifying Process Models.

State-space models. See Identifying State-Space Models.

You can also use `d2c` to
convert an estimated discrete-time model into a continuous-time model.

You can estimate all linear and nonlinear models supported by the System Identification Toolbox™ product as discrete-time models, except process models, which are defined only in continuous-time..

You can estimate both continuous-time and discrete-time models from time-domain data for linear and nonlinear differential and difference equations.

You can estimate discrete-time Hammerstein-Wiener and nonlinear ARX models from time-domain data.

You can also estimate nonlinear grey-box models from time-domain data. See Estimating Nonlinear Grey-Box Models.

There are two types of frequency-domain data:

Frequency response data

Frequency domain input/output signals which are Fourier Transforms of the corresponding time domain signals.

The data is considered continuous-time if its sample time (`Ts`)
is `0`, and is considered discrete-time if the sample
time is nonzero.

You can estimate the following types of continuous-time models directly:

Transfer function models using continuous- or discrete-time data. See Identifying Transfer Function Models.

Process models using continuous- or discrete-time data. See Identifying Process Models.

Input-output polynomial models of output-error structure using continuous time data. See Identifying Input-Output Polynomial Models.

State-space models using continuous- or discrete-time data.

From continuous-time frequency-domain data, you can only estimate continuous-time models.

You can also use `d2c` to
convert an estimated discrete-time model into a continuous-time model.

You can estimate all linear model types supported by the System Identification Toolbox product as discrete-time models, except process models, which are defined in continuous-time only. For estimation of discrete-time models, you must use discrete-time data.

The noise component of a model cannot be estimated using frequency
domain data, with the exception of ARX models. Thus, the *K* matrix
of an identified state-space model, the noise component, is zero.
An identified polynomial model has output-error (OE) or ARX structure;
BJ/ARMAX or other polynomial structure with nontrivial values of *C* or *D* polynomials
cannot be estimated.

For linear grey-box models, you can estimate both continuous-time
and discrete-time models from frequency-domain data. The noise component
of the model, the *K* matrix, cannot be estimated
using frequency domain data; it remains fixed to `0`.

Nonlinear grey-box models are supported only for time-domain data.

Nonlinear black box (nonlinear ARX and Hammerstein-Wiener models) cannot be estimated using frequency domain data.

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