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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
plotPlot input and output channels of iddata object
adviceAnalysis and recommendations for data or estimated linear models
delayestEstimate 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
realdataDetermine whether iddata is based on real-valued signals
feedbackIdentify possible feedback data
pexcitLevel of excitation of input signals
impulseestNonparametric impulse response estimation
etfeEstimate empirical transfer functions and periodograms
spaEstimate frequency response with fixed frequency resolution using spectral analysis
spafdrEstimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
iddataPlotOptionsOption set for plot when plotting data contained in an iddata object

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 the advice command 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, or dead 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.