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, specified as one of the following values:
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 