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Predict K-step ahead model output

This `predict`

command computes the
K-step ahead output of an identified model using measured input-output
data. To identify the model, you first collect all the input-output
data and then estimate the model parameters offline. To perform online
state estimation of a nonlinear system using real-time data, use the `predict`

command
for extended and unscented Kalman filters instead.

predicts
the output of an identified model `yp`

= predict(`sys`

,`data`

,`K`

)`sys`

, `K`

steps
ahead using the measured input-output data `data`

.

`predict`

command predicts the output response
over the time span of measured data. In contrast, `forecast`

performs prediction into the
future in a time range beyond the last instant of measured data. Use `predict`

to
validate `sys`

over the time span of measured data.

`predict(`

plots
the predicted output. Use with any of the previous input argument
combinations. To change display options in the plot, right-click the
plot to access the context menu. For more details about the menu,
see Tips.`sys`

,`data`

,`K`

___)

You can also plot the predicted model response using the `compare`

command.

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