Estimate Impulse-Response Models at the Command Line

Before you can perform this task, you must have:

Useimpulseestto compute impulse response models.impulseestestimates a high-order, noncausal FIR model using correlation analysis. The resulting models are stored asidtfmodel objects and contain impulse-response coefficients in the model numerator.

To estimate the modelmand plot the impulse or step response, use the following syntax:

m=impulseest(data,N); impulse(m,Time); step(m,Time);

wheredatais a single- or multiple-outputiddataoridfrdobject.Nis a scalar value specifying the order of the FIR system corresponding to the time range0:Ts:(N-1)*Ts, whereTsis the data sample time.

You can also specify estimation options, such as regularizing kernel, pre-whitening filter order and data offsets, usingimpulseestOptionsand pass them as an input toimpulseest. For example:

opt = impulseestOptions('RegularizationKernel','TC')); m = impulseest(data,N,opt);

To view the confidence region for the estimated response, useimpulseplotandstepplotto create the plot. Then useshowConfidence.

For example:

h = stepplot (m、时间);showConfidence (h, 3)% 3 std confidence region

Related Examples

• Identify Delay Using Transient-Response Plots
• 计算Response Values
• Estimate Impulse-Response Models Using System Identification App

More About

• What Is Time-Domain Correlation Analysis?
• Data Supported by Correlation Analysis
• Correlation Analysis Algorithm