Noise component of model
noise_model =
noise2meas(sys)
noise_model =
noise2meas(sys,noise)
returns the noise
component, noise_model
=
noise2meas(sys
)noise_model
, of a linear identified
model, sys
. Use noise2meas
to
convert a timeseries model (no inputs) to an input/output model.
The converted model can be used for linear analysis, including viewing
pole/zero maps, and plotting the step response.
specifies
the noise variance normalization method.noise_model
=
noise2meas(sys
,noise
)

Identified linear model. 

Noise variance normalization method.
Default: 

Noise component of
$$y(t)=Gu(t)+He(t)$$ G is the transfer function between the measured input, u(t), and the output, y(t). H is the noise model and describes the effect of the disturbance, e(t), on the model's response. An equivalent statespace representation of $$\begin{array}{l}\dot{x}(t)=Ax(t)+Bu(t)+Ke(t)\\ y(t)=Cx(t)+Du(t)+e(t)\\ e(t)=Lv(t)\end{array}$$ v(t) is white noise with
independent channels and unit variances. The whitenoise signal e(t)
represents the model's innovations and has variance LL^{T}.
The noisevariance data is stored using the
The model type of
To obtain the model coefficients of 