线性模型辨识基础知识
辨识线性模型、选择合适的模型结构、构建和修改模型对象结构以及使用正则化估计的基本信息
线性模型是您可以使用 System Identification Toolbox™ 辨识的最简单模型。当线性模型足以完全捕获系统动态特性时，可使用线性模型辨识。要辨识线性模型，可以先从时域或频域输入-输出数据和模型结构（例如状态空间或传递函数模型）入手。软件会反复调整自由模型参数，以尽量减少测量输出与仿真模型对输入数目据的响应之间的差异。该工具箱允许您执行如下任务：
使用特定的模型结构估计线性模型。
使用黑盒建模方法并探索最适合您的数据的模型结构。
构建初步的线性模型，并用它来初始化要估计的模型的参数。
通过将已知参数固定为特定值，将系统知识融入到您的模型中。
使用正则化估计通过限制模型灵活性来减少模型中的不确定性。
主题
辨识线性模型
- Identify Linear Models Using System Identification App
Identify linear black-box models from single-input/single-output (SISO) data using the System Identification app. - Identify Linear Models Using the Command Line
Identify linear models from multiple-input/single-output (MISO) data using System Identification Toolbox commands. - Frequency Domain Identification: Estimating Models Using Frequency Domain Data
This example shows how to estimate models using frequency domain data. - Estimation Report
The estimation report contains information about the results and options used for a model estimation.
选择模型结构
- About Identified Linear Models
System Identification Toolbox software uses objects to represent a variety of linear and nonlinear model structures. - Available Linear Models
Summary of linear model types that you can use for system identification. - 黑盒建模
当您的主要关注点是拟合数据，而不考虑模型的具体数学结构时，黑盒建模就很有用。 - Model Structure Selection: Determining Model Order and Input Delay
This example shows some methods for choosing and configuring the model structure. - Modeling Multiple-Output Systems
Use a multiple-output modeling technique that suits the complexity and internal input-output coupling of your system. - 模型对象的类型
模型对象类型包括数值模型（用于表示具有固定系数的系统）和广义模型（用于表示具有可调或不确定系数的系统）。
模型对象结构和约束
- Linear Model Structures
Linear models in System Identification Toolbox take the form of model objects that are linear model structures. You can construct model objects directly or use estimation commands to both construct and estimate models. You can also modify the properties of existing model objects. - Imposing Constraints on Model Parameter Values
Constrain the adjustments that the estimation algorithm can make to individual model parameters by using theStructureproperty of the mode object.
正则化
- Regularized Identification of Dynamic Systems
This example shows the benefits of regularization for identification of linear and nonlinear models. - Estimate Regularized ARX Model Using System Identification App
This example shows how to estimate regularized ARX models using automatically generated regularization constants in the System Identification app. - Regularized Estimates of Model Parameters
Regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.
其他主题
- Loss Function and Model Quality Metrics
Configure the loss function that is minimized during parameter estimation. After estimation, use model quality metrics to assess the quality of identified models. - Effect of Input Intersample Behavior on Continuous-Time Models
The intersample behavior of the input signals influences the estimation, simulation and prediction of continuous-time models.
精选示例
System Identification Toolbox 中的数据和模型对象
本示例演示了如何管理 System Identification Toolbox 中可用的数据和模型对象。
Estimating Simple Models from Real Laboratory Process Data
Develop and analyze simple models from a real laboratory process data.
