Neural State-Space Models
Live Editor Tasks
|Estimate Neural State-Space Model
|Estimate neural state-space model in the Live Editor (Since R2023b)
|Create and initialize a Multi-Layer Perceptron (MPL) network to be used within a neural state-space system (Since R2022b)
|Create training options object for neural state-space systems (Since R2022b)
|Estimate nonlinear state-space model using measured time-domain system data (Since R2022b)
|Generate MATLAB functions that evaluate the state and output functions of a neural state-space object, and their Jacobians (Since R2022b)
|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)
|Linearize a neural state-space model around an operating point (Since R2022b)
|Simulate response of identified model
|Neural State-Space Model
|Simulate neural state-space model in Simulink (Since R2022b)
- About Identified Nonlinear Models
Dynamic models in System Identification Toolbox™ software are mathematical relationships between the inputs u(t) and outputs y(t) of a system.
- 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.