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신경망 상태공간 모델
R2026a신경망 상태공간 모델은 신경망을 사용하여 상태 천이와 측정 함수를 모델링하는 비선형 상태공간 모델의 한 유형입니다. System Identification Toolbox™를 사용하여 이러한 신경망의 가중치와 편향을 식별할 수 있습니다. 제어, 추정, 최적화, 차수 축소 모델링을 위해 훈련된 모델을 사용할 수 있습니다.
라이브 편집기 작업
| 신경망 상태공간 모델 추정 | Estimate neural state-space model in the Live Editor (R2023b 이후) |
함수
createMLPNetwork | Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (R2022b 이후) |
setNetwork | Assign dlnetwork object as the state or output function of a
neural state-space model (R2024b 이후) |
nssTrainingOptions | Create training options object for neural state-space systems (R2022b 이후) |
nlssinit | Initialize nonlinear state-space model using measured time-domain system data (R2026a 이후) |
nlssest | Estimate nonlinear state-space model using measured time-domain system data (R2022b 이후) |
generateMATLABFunction | Generate MATLAB functions that evaluate the state and output functions, and their Jacobians, of a nonlinear grey-box or neural state-space model (R2022b 이후) |
idNeuralStateSpace/evaluate | Evaluate a neural state-space system for a given set of state and input values and return state derivative (or next state) and output values (R2022b 이후) |
idNeuralStateSpace/linearize | Linearize a neural state-space model around an operating point (R2022b 이후) |
sim | Simulate response of identified model |
객체
idNeuralStateSpace | Neural state-space model with identifiable network weights (R2022b 이후) |
nssTrainingADAM | Adam training options object for neural state-space systems (R2022b 이후) |
nssTrainingSGDM | SGDM training options object for neural state-space systems (R2022b 이후) |
nssTrainingRMSProp | RMSProp training options object for neural state-space systems (R2024b 이후) |
nssTrainingLBFGS | L-BFGS training options object for neural state-space systems (R2024b 이후) |
블록
 | Neural State-Space Model | Simulate neural state-space model in Simulink (R2022b 이후) |
도움말 항목
- What Are Neural State-Space Models?
Understand the structure of a neural state-space model.
- Training Neural State-Space Models
Approaches to train neural state-space 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.
- Neural State-Space Model of Simple Pendulum System
This example shows how to design and train a deep neural network that approximates a nonlinear state-space system in continuous time.
- Augment Known Linear Model with Flexible Nonlinear Functions
This example demonstrates a method to improve the normalized root mean-squared error (NRMSE) fit score of an existing state-space model using a neural state-space model.
- Reduced Order Modeling of a Nonlinear Dynamical System Using Neural State-Space Model with Autoencoder
This example shows reduced order modeling of a nonlinear dynamical system using a neural state-space (NSS) modeling technique.
- 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.
