预处理数据
函数
detrend | Subtract offset or trend from time-domain signals contained in
iddata objects |
retrend | Add offsets or trends to time-domain data signals stored in iddata objects |
diff | iddata 对象中的差异信号 |
idfilt | Filter data using user-defined passbands, general filters, or Butterworth filters |
misdata | Reconstruct missing input and output data |
nkshift | Shift data sequences |
idresamp | Resample time-domain data by decimation or interpolation |
idresampOptions | Option set for idresamp (自 R2023a 起) |
resample | (Not recommended) Resample time-domain data that is stored in an iddata object by decimation or interpolation (requires Signal Processing Toolbox software) |
getTrend | Create trend information object to store offset, mean, and trend information for
time-domain signals stored in iddata object |
chgFreqUnit | Change frequency units of frequency-response data model |
fdel | Delete specified data from frequency response data (FRD) models |
TrendInfo | Offset and linear trend slope values for detrending data |
示例和操作指南
- Preprocess Data Using Quick Start
Subtract mean values from data, and specify estimation and validation data.
- 提取并建模特定数据段
此示例显示如何通过仅合并准确的数据段并忽略其余数据段来创建多试验、时域数据集。
- How to Detrend Data Using the App
Before you can perform this task, you must have regularly-sampled, steady-state time-domain data imported into the System Identification app.
- How to Detrend Data at the Command Line
Before you can perform this task, you must have time-domain data as an
iddataobject. - Resampling Data Using the App
Use the System Identification app to resample time-domain data.
- Resampling Data at the Command Line
Decimate and interpolate time-domain data.
- How to Filter Data Using the App
The System Identification app lets you filter time-domain data using a fifth-order Butterworth filter by enhancing or selecting specific passbands.
- How to Filter Data at the Command Line
Use
idfiltto apply passband and other custom filters to a time-domain or a frequency-domainiddataobject.
概念
- Handling Missing Data and Outliers
Handling missing or erroneous data values.
- Handling Offsets and Trends in Data
Removing and restoring constant offsets and linear trends in data signals.
- Resampling Data
Decimating and interpolating (resampling) data.
- Filtering Data
Deciding whether to filter data before model estimation and how to prefilter data.
