降阶建模
通过创建准确的替代项来降低模型的计算复杂度
降阶建模用于降低模型的计算复杂度或存储需求，同时将预期的保真度保持在令人满意的误差范围内。使用替代降阶模型可以简化分析和控制设计。
主题
降阶建模基础知识
- 降阶建模
通过创建准确的替代品来降低模型的计算复杂性。
数据驱动的方法
- Nonlinear ARX Model of SI Engine Torque Dynamics
This example describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a nonlinear ARX model. - Hammerstein-Wiener Model of SI Engine Torque Dynamics
This example describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a Hammerstein-Wiener model. - 火花塞发动机扭矩动力学的神经状态空间模型
此示例使用神经状态空间模型描述了火花点火 (SI) 发动机的非线性扭矩动力学的降阶建模 (ROM)。所确定的模型可用于硬件在环 (HIL) 测试、动力系统控制、诊断和训练算法设计。例如，您可以使用该模型进行后处理控制和诊断算法开发。有关神经状态空间模型的详细信息，请参阅Neural State-Space Models。 - Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model
This example shows a reduced order modeling (ROM) workflow, where you use deep learning to obtain a low-order nonlinear state-space model that serves as a surrogate for a high-fidelity battery model.
基于线性化的方法
- Specify Linearization for Model Components Using System Identification (Simulink Control Design)
You can use System Identification Toolbox™ software to identify a linear system for a model component that does not linearize well, and use the identified system to specify its linearization. - Reduced Order Modeling of a Nonlinear Dynamical System as an Identified Linear Parameter Varying Model
Identify a linear parameter varying reduced order model of a cascade of nonlinear mass-spring-damper systems.
