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Modèles ARX non linéaires
Utilisez des modèles ARX non linéaires pour représenter les non-linéarités de votre système au moyen d’estimateurs de non-linéarité tels que les réseaux d'ondelettes, le partitionnement d’arbres et les réseaux sigmoïdes. Pour estimer des modèles ARX non linéaires, utilisez l’application System Identification ou la fonction nlarx.
Applications
| System Identification | Identifier des modèles de systèmes dynamiques à partir de données mesurées |
Fonctions
Blocs
Rubriques
- What Are Nonlinear ARX Models?
Understand the structure of a nonlinear ARX model.
- Available Mapping Functions for Nonlinear ARX Models
Choose from sigmoid, wavelet, tree partition, linear, neural, and custom network nonlinearities.
- Identifying Nonlinear ARX Models
Specify the Nonlinear ARX structure, and configure the estimation algorithm.
- Train NARX Networks Using idnlarx Instead of narxnet
Use
idnlarxas a modern alternative tonarxnetfor estimating nonlinear ARX models. - NARMAX Model Identification
Generate NARMAX models using available model structures and their training algorithms.
- Validate Nonlinear ARX Models
Plot model nonlinearities, analyze residuals, and simulate and predict model output.
- Using Nonlinear ARX Models
Simulate, predict, and forecast model output, linearize nonlinear ARX models, and import estimated models into the Simulink® software.
- Linear Approximation of Nonlinear Black-Box Models
Choose the approach for computing linear approximations, compute operating points for linearization, and linearize your model.
- How the Software Computes Nonlinear ARX Model Output
How the software evaluates the output of nonlinearity estimators and uses this output to compute the model response.
Sélection d՚exemples
A Tutorial on Identification of Nonlinear ARX and Hammerstein-Wiener Models
Identify single-input-single-output (SISO) nonlinear black box models using measured input-output data.
Create Black-Box Models of a Robotic Arm System Using Neural Networks
Use neural networks as the nonlinear function when you model nonlinear ARX and Hammerstein-Wiener models. It compares the nonlinear systems estimated using different types of neural networks, activation functions, and number of layers.
Building Nonlinear ARX Models with Nonlinear and Custom Regressors
Use custom regressors in SISO and MIMO nonlinear ARX (IDNLARX) models.
Machine-Learning-Based Identification of Two-Tank System
Identify a black-box dynamic model of a nonlinear two-tank system using machine learning.
Machine-Learning-Based Identification of Nonlinear Magneto-Rheological Fluid Damper
Estimate a black-box dynamic model of a nonlinear magneto-rheological (MR) fluid damper using machine learning.
Surrogate Modeling Using Gaussian Process-Based NLARX Model
In this example, you replace a hydraulic cavitation cycle model in Simulink with a surrogate nonlinear ARX (NLARX) model to facilitate faster simulation.
Piezoelectric Actuator Model Identification Using Machine Learning
Identify a piezoelectric actuator nonlinear model using machine-learning-based techniques that include support vector machines and Gaussian process functions.
Nonlinear ARX Model of SI Engine Torque Dynamics
Describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a nonlinear ARX model. The identified model can be used for hardware-in-the-loop (HIL) testing, powertrain control, diagnostic, and training algorithm design. For example, you can use the model for aftertreatment control and diagnostics algorithm development. For more information on nonlinear ARX models, see Nonlinear ARX Models.
Predict EV Battery Temperature Using Cascade-Correlation Model
Simulate the battery temperature on an electric vehicle (EV) model by using a cascade-correlation neural network in a nonlinear ARX model.
