Main Content

データの解析

遅延、フィードバック、励起レベルなどのデータ特性の決定

関数

bode 周波数応答、または振幅と位相データのボード線図
bodemag 周波数応答の振幅のみのボード線図
plot Plot input and output channels ofiddataobject
advice Analysis and recommendations for data or estimated linear models
delayest Estimate time delay (dead time) from data
isreal モデル パラメーターまたはデータ値が実数かどうかを判別
realdata Determine whetheriddatais based on real-valued signals
feedback Identify possible feedback data
pexcit Level of excitation of input signals
impulseest Nonparametric impulse response estimation
etfe Estimate empirical transfer functions and periodograms
spa Estimate frequency response with fixed frequency resolution using spectral analysis
spafdr Estimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
iddataPlotOptions Option set forplotwhen plotting data contained in aniddataobject

例および操作のヒント

  • How to Plot Data in the App

    After importing data into the System Identification app, as described in データの表現, you can plot the data.

  • How to Plot Data at the Command Line

    The following table summarizes the commands available for plotting time-domain, frequency-domain, and frequency-response data.

  • How to Analyze Data Using the advice Command

    You can use theadvicecommand to analyze time- or frequency- domain data before estimating a model. The resulting report informs you about the possible need to preprocess the data and identifies potential restrictions on the model accuracy. You should use these recommendations in combination with plotting the data and validating the models estimated from this data.

  • Identify Delay Using Transient-Response Plots

    You can use transient-response plots to estimate the input delay, ordead time, of linear systems. Input delay represents the time it takes for the output to respond to the input.

概念

  • Is Your Data Ready for Modeling?

    Before you start estimating models from data, you should check your data for the presence of any undesirable characteristics. For example, you might plot the data to identify drifts and outliers. You plot analysis might lead you to preprocess your data before model estimation.