神经状态空间模型
神经状态空间模型是一种非线性状态空间模型，其中状态转换和测量函数使用神经网络建模。您可以使用 System Identification Toolbox™ 软件识别这些网络的权重和偏置。您可以使用经过训练的模型进行控制、估计、优化和降阶建模。
实时编辑器任务
| 估计神经状态空间模型 | Estimate neural state-space model in the Live Editor (自 R2023b 起) |
函数
createMLPNetwork | Create and initialize a Multi-Layer Perceptron (MLP) network to be used within a neural state-space system (自 R2022b 起) |
setNetwork | Assign dlnetwork object as the state or output function of a
neural state-space model (自 R2024b 起) |
nssTrainingOptions | 为神经状态空间系统创建训练选项对象 (自 R2022b 起) |
nlssest | Estimate nonlinear state-space model using measured time-domain system data (自 R2022b 起) |
generateMATLABFunction | Generate MATLAB functions that evaluate the state and output functions, and their Jacobians, of a nonlinear grey-box or neural state-space model (自 R2022b 起) |
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 (自 R2022b 起) |
idNeuralStateSpace/linearize | Linearize a neural state-space model around an operating point (自 R2022b 起) |
sim | Simulate response of identified model |
对象
idNeuralStateSpace | Neural state-space model with identifiable network weights (自 R2022b 起) |
nssTrainingADAM | Adam training options object for neural state-space systems (自 R2022b 起) |
nssTrainingSGDM | SGDM training options object for neural state-space systems (自 R2022b 起) |
nssTrainingRMSProp | RMSProp training options object for neural state-space systems (自 R2024b 起) |
nssTrainingLBFGS | L-BFGS training options object for neural state-space systems (自 R2024b 起) |
模块
| Neural State-Space Model | Simulate neural state-space model in Simulink (自 R2022b 起) |
主题
- What Are Neural State-Space Models?
Understand the structure of a neural state-space model.
- 火花塞发动机扭矩动力学的神经状态空间模型
此示例使用神经状态空间模型描述了火花点火 (SI) 发动机的非线性扭矩动力学的降阶建模 (ROM)。所确定的模型可用于硬件在环 (HIL) 测试、动力系统控制、诊断和训练算法设计。例如，您可以使用该模型进行后处理控制和诊断算法开发。有关神经状态空间模型的详细信息，请参阅Neural State-Space Models。
- 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.
- 利用灵活的非线性函数增强已知线性模型
此示例演示了一种使用神经状态空间模型来改进现有状态空间模型的归一化均方根误差 (NRMSE) 拟合分数的方法。
- 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.
