Documentation |
Noise component of model
noise_model =
noise2meas(sys)
noise_model =
noise2meas(sys,noise)
noise_model = noise2meas(sys) returns the noise component, noise_model, of a linear identified model, sys. Use noise2meas to convert a time-series 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.
noise_model = noise2meas(sys,noise) specifies the noise variance normalization method.
noise_model |
Noise component of sys. sys represents the system $$y(t)=Gu(t)+He(t)$$ 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 state-space representation of sys is $$\begin{array}{l}\dot{x}(t)=Ax(t)+Bu(t)+Ke(t)\\ y(t)=Cx(t)+Du(t)+e(t)\\ e(t)=Lv(t)\end{array}$$ v(t) is white noise with independent channels and unit variances. The white-noise signal e(t) represents the model's innovations and has variance LL^{T}. The noise-variance data is stored using the NoiseVariance property of sys.
The model type of noise_model depends on the model type of sys.
To obtain the model coefficients of noise_model in state-space form, use ssdata. Similarly, to obtain the model coefficients in transfer-function form, use tfdata. |