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차수 축소 모델링
차수 축소 모델링(ROM) 작업에 Deep Learning Toolbox™를 사용합니다.
차수 축소 모델링(ROM)은 계산 복잡도를 줄여 복잡한 고충실도 모델과 시뮬레이션을 단순화하면서도 모델 동작과 정확성을 그대로 유지할 수 있는 기법입니다. 예를 들어 Simulink 모델에서 연산량이 많은 서브시스템을 현실적 예측을 수행하는 훈련된 신경망으로 대체할 수 있습니다.
함수
exportNetworkToSimulink | Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (R2024b 이후) |
블록
| Predict | 훈련된 딥러닝 신경망을 사용하여 응답 변수 예측 |
| Stateful Predict | Predict responses using a trained recurrent neural network (R2021a 이후) |
도움말 항목
- Physical System Modeling Using LSTM Network in Simulink
This example shows how to create a reduced order model (ROM) that acts as a virtual sensor in a Simulink® model using a long short-term memory (LSTM) neural network.
- Reduced Order Modeling Using Continuous-Time Echo State Network
This example shows how to train a continuous-time echo state network (CTESN) model to solve Robertson's equation.
- Generate Deep Learning SI Engine Model (Powertrain Blockset)
Generate a deep learning SI engine model from measured transient engine data.
- Implement Unsupported Deep Learning Layer Blocks
This example shows how to implement layers using Simulink blocks or MATLAB code in a MATLAB Function block.
추천 예제
Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model
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.
