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
idplotPlot input and output channels of estimation data (R2023a 以降)
adviceAnalysis and recommendations for data or estimated linear models
delayestデータからの時間遅延 (むだ時間) の推定
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
checkFeedbackIdentify possible feedback data (R2023a 以降)
pexcitLevel of excitation of input signals
impulseestNonparametric impulse response estimation
etfe経験的な伝達関数とピリオドグラムの推定
spaスペクトル解析を使用した固定の周波数分解能による周波数応答の推定
spafdrEstimate frequency response and spectrum using spectral analysis with frequency-dependent resolution
dataPlotOptionsOption set for idplot when plotting input/output estimation data contained in a timetable, numeric matrices, or an iddata object (R2023a 以降)

例および使用方法

  • 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.