|
|
|
| R2011b Documentation → System Identification Toolbox | |
Learn more about System Identification Toolbox |
|
| Contents | Index |
This table summarizes what's new in Version 7.3 (R2009a):
| New Features and Changes | Version Compatibility Considerations | Fixed Bugs and Known Problems |
|---|---|---|
| Yes Details below | No | Bug Reports |
New features introduced in this version:
This version of the product includes new and expanded functionality for handling offsets and trends in signals. This data processing operation is necessary for estimating more accurate linear models because linear models cannot capture arbitrary differences between the input and output signal levels.
The previous version of the product let you remove mean values or linear trends from steady-state signals using the GUI and the detrend function. For transient signals, you had to remove offsets and trends using matrix manipulation.
The GUI functionality for removing means and linear trends from signals is unchanged. However, you can now do the following at the command line:
Save the values of means or linear trends removed during detrending using a new detrend output argument. You can use this saved trend information to detrend other data sets. You can also restore subtracted trends to the output simulated by a linear model that was estimated from detrended data.
For example, this syntax computes and removes mean values from the data, and saves these values to the output variable T: [data_d,T]=detrend(data). T is an object with properties that store offset and slope information for input and output signals.
Remove any offset or linear trend from the data using a new detrend input argument. This is useful for removing arbitrary nonzero offsets from transient data or applying previously saved trend information to any data set.
For example, this syntax removes an offset or trend specified by T: data_d = detrend(data,T).
Add an arbitrary offset or linear trend to data signals. This is useful when you want to simulate the response of a linear model about a nonzero equilibrium input-output level and this model was estimated from detrended data.
For example, this syntax adds trend information to a simulated model output y_sim, which is an iddata object: y = retrend(y_sim,T). T specifies the offset and slope information for inputs and outputs.
For more information, see Handling Offsets and Trends in Data.
The getreg command can now return the numerical values of regressors in nonlinear ARX models and provides an intermediate output of nonlinear ARX models.
This advanced functionality converts input and output values to regressors, and passes the regressor values to the evaluate command to compute the model response. This incremental step lets you gain insight into the propagation of information through the nonlinear ARX model.
For more information, see the getreg reference page. To learn more about the nonlinear ARX model structure, see Nonlinear Black-Box Model Identification.
 | Version 7.3.1 (R2009b) System Identification Toolbox Software | Version 7.2.1 (R2008b) System Identification Toolbox Software |  |
Learn more about resources for designing, testing, and implementing control systems.
Get free kit| © 1984-2012- The MathWorks, Inc. - Site Help - Patents - Trademarks - Privacy Policy - Preventing Piracy - RSS |