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低次元化されたモデル化

正確な代理を作成してモデルの計算量を削減する

低次元化されたモデル化は、満足できる誤差内で予期される忠実度を保持しながら、モデルの計算量やストレージ要件を削減する手法です。低次元化された代理モデルを使用すると、解析および制御設計を簡略化できます。

トピック

低次元化されたモデル化の基本

データ駆動型の手法

  • 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. The identified model can be used for hardware-in-the-loop (HIL) testing, powertrain control, diagnostic, and training algorithm design. For example, you can use the model for aftertreatment control and diagnostics algorithm development. For more information on nonlinear ARX models, see Nonlinear ARX Models.
  • 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. The identified model can be used for hardware-in-the-loop (HIL) testing, powertrain control, diagnostic, and training algorithm design. For example, you can use the model for aftertreatment control and diagnostics algorithm development. For more information on Hammerstein-Wiener models, see Hammerstein-Wiener Models.
  • 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. The identified model can be used for hardware-in-the-loop (HIL) testing, powertrain control, diagnostics, and training algorithm design. For example, you can use the model for after-treatment control and diagnostic algorithm development. For more information on neural state-space models, see 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. The low-order model takes the current (charge or discharge) and state of charge (SOC) as inputs and predicts voltage and temperature of an electric vehicle (EV) battery module while the battery is being cooled by an edge-cooled plate with a coolant at a constant flow rate. You train the low-order model and deploy it in Simulink® to compare it against the high-fidelity model.

線形化ベースの手法