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ニューラル状態空間モデル
ニューラル状態空間モデルは、非線形状態空間モデルの一種で、状態遷移関数と測定関数をニューラル ネットワークを使用してモデル化したものです。System Identification Toolbox™ ソフトウェアを使用して、それらのネットワークの重みとバイアスを識別できます。学習させたモデルを制御、推定、最適化、および低次元化モデリングに使用できます。
ライブ エディター タスク
| ニューラル状態空間モデルの推定 | Estimate neural state-space model in the Live Editor (R2023b 以降) |
関数
createMLPNetwork | Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (R2022b 以降) |
setNetwork | Assign dlnetwork object as the state or output function of a
neural state-space model (R2024b 以降) |
nssTrainingOptions | Create training options object for neural state-space systems (R2022b 以降) |
nlssest | Estimate nonlinear state-space model using measured time-domain system data (R2022b 以降) |
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 以降) |
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 以降) |
idNeuralStateSpace/linearize | Linearize a neural state-space model around an operating point (R2022b 以降) |
sim | 同定されたモデルの応答のシミュレーション |
オブジェクト
idNeuralStateSpace | Neural state-space model with identifiable network weights (R2022b 以降) |
nssTrainingADAM | Adam training options object for neural state-space systems (R2022b 以降) |
nssTrainingSGDM | SGDM training options object for neural state-space systems (R2022b 以降) |
nssTrainingRMSProp | RMSProp training options object for neural state-space systems (R2024b 以降) |
nssTrainingLBFGS | L-BFGS training options object for neural state-space systems (R2024b 以降) |
ブロック
| Neural State-Space Model | Simulate neural state-space model in Simulink (R2022b 以降) |
トピック
- 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.
