Neural State-Space Models

Use neural networks to represent the functions defining the nonlinear state space realization of your system

Tâches du Live Editor

Estimate neural state-space model in the Live Editor (depuis R2023b)

`createMLPNetwork`	Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (depuis R2022b)
`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 of a neural state-space object, and their Jacobians (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

`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)

Simulate neural state-space model in Simulink (depuis 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.