MathWorks Machine Translation
The automated translation of this page is provided by a general purpose third party translator tool.
MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation.
This example shows linear model identification of a glass tube manufacturing process. The experiments and the data are discussed in:
This example shows how to estimate a transfer function from measured signal data.
This example shows how to estimate a transfer function from frequency response data. You use Simulink® Control Design™ to collect frequency response data from a Simulink model and the tfest command to estimate a transfer function from the measured data. To run the example with previously saved frequency response data start from the Estimating a Transfer Function section.
This example shows computation of bending modes of a flexible wing aircraft. The vibration response of the wing is collected at multiple points along its span. The data is used to identify a dynamic model of the system. The modal parameters are extracted from the identified model. The modal parameter data is combined with the sensor position information to visualize the various bending modes of the wing. This example requires Signal Processing Toolbox (TM).
This example shows several identification methods available in System Identification Toolbox™. We begin by simulating experimental data and use several estimation techniques to estimate models from the data. The following estimation routines are illustrated in this example: spa, ssest, tfest, arx, oe, armax and bj.
This example illustrates how models simulated in Simulink® can be identified using System Identification Toolbox™. The example describes how to deal with continuous-time systems and delays, as well as the importance of the intersample behavior of the input.
This example shows how to obtain linear approximations of a complex, nonlinear system by means of linear model identification. The approach is based on selection of an input signal that excites the system. A linear approximation is obtained by fitting a linear model to the simulated response of the nonlinear model for the chosen input signal.
This example shows how to deal with data with several input and output channels (MIMO data). Common operations, such as viewing the MIMO data, estimating and comparing models, and viewing the corresponding model responses are highlighted.
You clicked a link that corresponds to this MATLAB command:
Run the command by entering it in the MATLAB Command Window.
Web browsers do not support MATLAB commands.
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Contact your local office
Get trial now