차수 축소 모델링

정확한 대리(surrogate)를 생성하여 모델의 계산 복잡도 축소

차수 축소 모델링은 모델의 계산 복잡도나 저장 요구 사항을 줄이면서 만족스러운 오차 내에서 예상된 충실도를 유지하기 위한 기법입니다. 대리 차수 축소 모델(surrogate reduced order model)로 작업하면 분석 및 제어 설계가 단순화될 수 있습니다.

도움말 항목

Reduced Order Modeling
Reduce computational complexity of models by creating accurate surrogates.

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.
Neural State-Space Model of SI Engine Torque Dynamics
This example describes reduced order modeling (ROM) of the nonlinear torque dynamics of a spark-ignition (SI) engine using a neural state-space model.

LPV Approximation of Boost Converter Model (Simulink Control Design)
Approximate a nonlinear Simscape™ Electrical™ model using a linear parameter varying model.
Reduce Model Order Using Model Reducer App (Control System Toolbox)
Interactively reduce model order while preserving important dynamics.
Sparse Modal Truncation of Linearized Structural Beam Model (Control System Toolbox)
Compute a low-order approximation of a sparse state-space model obtained from linearizing a structural beam model. (R2023b 이후)
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.