Modelli stato-spazio neurali
I modelli stato-spazio neurali sono un tipo di modelli stato-spazio non lineari in cui le funzioni di transizione di stato e di misura sono modellate utilizzando le reti neurali. È possibile identificare i pesi e i bias di queste reti utilizzando il software System Identification Toolbox™. È possibile utilizzare il modello addestrato per il controllo, la stima, l'ottimizzazione e la modellazione di ordine ridotto.
Attività di Live Editor
| Stima del modello stato-spazio neurale | Estimate neural state-space model in the Live Editor (Da R2023b) |
Funzioni
createMLPNetwork | Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (Da R2022b) |
setNetwork | Assign dlnetwork object as the state or output function of a
neural state-space model (Da R2024b) |
nssTrainingOptions | Create training options object for neural state-space systems (Da R2022b) |
nlssest | Estimate nonlinear state-space model using measured time-domain system data (Da 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 (Da 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 (Da R2022b) |
idNeuralStateSpace/linearize | Linearize a neural state-space model around an operating point (Da R2022b) |
sim | Simulate response of identified model |
Oggetti
idNeuralStateSpace | Neural state-space model with identifiable network weights (Da R2022b) |
nssTrainingADAM | Adam training options object for neural state-space systems (Da R2022b) |
nssTrainingSGDM | SGDM training options object for neural state-space systems (Da R2022b) |
nssTrainingRMSProp | RMSProp training options object for neural state-space systems (Da R2024b) |
nssTrainingLBFGS | L-BFGS training options object for neural state-space systems (Da R2024b) |
Blocchi
| Neural State-Space Model | Simulate neural state-space model in Simulink (Da R2022b) |
Argomenti
- 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.
