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Modèles neuronaux de représentation d'état
Les modèles neuronaux de représentation d'état constituent un type de modèles non linéaires de représentation d'état dans lesquels les fonctions de transition d'état et de mesure sont modélisées au moyen de réseaux de neurones. Vous pouvez identifier les poids et biais de ces réseaux au moyen du software System Identification Toolbox™. Vous pouvez utiliser le modèle formé pour le contrôle, l'estimation, l'optimisation et la modélisation d'ordre réduit.
Tâches du Live Editor
| Estimer un modèle neuronal de représentation d'état | Estimate neural state-space model in the Live Editor (depuis R2023b) |
Fonctions
createMLPNetwork | Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (depuis R2022b) |
setNetwork | Assign dlnetwork object as the state or output function of a
neural state-space model (depuis R2024b) |
nssTrainingOptions | Create training options object for neural state-space systems (depuis R2022b) |
nlssest | Estimate nonlinear state-space model using measured time-domain system data (depuis R2022b) |
generateMATLABFunction | Generate MATLAB functions that evaluate the state and output functions, and their Jacobians, of a nonlinear grey-box or neural state-space model (depuis R2022b) |
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 (depuis R2022b) |
idNeuralStateSpace/linearize | Linearize a neural state-space model around an operating point (depuis R2022b) |
sim | Simulate response of identified model |
Objets
idNeuralStateSpace | Neural state-space model with identifiable network weights (depuis R2022b) |
nssTrainingADAM | Adam training options object for neural state-space systems (depuis R2022b) |
nssTrainingSGDM | SGDM training options object for neural state-space systems (depuis R2022b) |
nssTrainingRMSProp | RMSProp training options object for neural state-space systems (depuis R2024b) |
nssTrainingLBFGS | L-BFGS training options object for neural state-space systems (depuis R2024b) |
Blocs
| Neural State-Space Model | Simulate neural state-space model in Simulink (depuis R2022b) |
Rubriques
- What Are Neural State-Space Models?
Understand the structure of a neural state-space 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.
- 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.
