신경망 상태공간 모델

시스템의 비선형 상태공간 실현을 정의하는 함수를 나타내기 위해 신경망 사용

라이브 편집기 작업

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

`createMLPNetwork`	Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (R2022b 이후)
`nssTrainingOptions`	Create training options object for neural state-space systems (R2022b 이후)
`nlssest`	Estimate nonlinear state-space model using measured time-domain system data (R2022b 이후)
`generateMATLABFunction`	Generate MATLAB functions that evaluate the state and output functions, and their Jacobians, of a nonlinear grey-box or neural state-space model (R2022b 이후)
`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 (R2022b 이후)
`idNeuralStateSpace/linearize`	Linearize a neural state-space model around an operating point (R2022b 이후)
`sim`	Simulate response of identified model

`idNeuralStateSpace`	Neural state-space model with identifiable network weights (R2022b 이후)
`nssTrainingADAM`	Adam training options object for neural state-space systems (R2022b 이후)
`nssTrainingSGDM`	SGDM training options object for neural state-space systems (R2022b 이후)

Simulate neural state-space model in Simulink (R2022b 이후)

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.
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.