Neural State-Space Models
Neural state-space models are a type of nonlinear state-space models where the state-transition and measurement functions are modeled using neural networks. You can identify the weights and biases of these networks using System Identification Toolbox™ software. You can use the trained model for control, estimation, optimization, and reduced order modeling.
Live Editor Tasks
| Estimate Neural State-Space Model | Estimate neural state-space model in the Live Editor (Since R2023b) |
Functions
createMLPNetwork | Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (Since R2022b) |
setNetwork | Assign dlnetwork object as the state or output function of a
neural state-space model (Since R2024b) |
nssTrainingOptions | Create training options object for neural state-space systems (Since R2022b) |
nlssest | Estimate nonlinear state-space model using measured time-domain system data (Since 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 (Since 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 (Since R2022b) |
idNeuralStateSpace/linearize | Linearize a neural state-space model around an operating point (Since R2022b) |
sim | Simulate response of identified model |
Objects
idNeuralStateSpace | Neural state-space model with identifiable network weights (Since R2022b) |
nssTrainingADAM | Adam training options object for neural state-space systems (Since R2022b) |
nssTrainingSGDM | SGDM training options object for neural state-space systems (Since R2022b) |
nssTrainingRMSProp | RMSProp training options object for neural state-space systems (Since R2024b) |
nssTrainingLBFGS | L-BFGS training options object for neural state-space systems (Since R2024b) |
Blocks
| Neural State-Space Model | Simulate neural state-space model in Simulink (Since R2022b) |
Topics
- 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.
