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Modelli Hammerstein-Wiener

Connessione di sistemi dinamici lineari con non linearità statiche, come la saturazione e la zona morta

Utilizzare i modelli Hammerstein-Wiener per stimare le non linearità statiche in un sistema altrimenti lineare. È possibile utilizzare la struttura di Hammerstein-Wiener per catturare gli effetti fisici non lineari nei sensori e negli attuatori che influenzano l'input e l'output di un sistema lineare, come le zone morte e la saturazione. Per stimare i modelli Hammerstein-Wiener, utilizzare l'app System Identification o la funzione nlhw.

App

System IdentificationIdentify models of dynamic systems from measured data

Funzioni

espandi tutto

idnlhwHammerstein-Wiener Model
nlhwEstimate Hammerstein-Wiener model
nlhwOptionsOption set for nlhw
initSet or randomize initial parameter values
getpvecObtain model parameters and associated uncertainty data
setpvecModify values of model parameters
idCustomNetworkCustom network function for nonlinear ARX and Hammerstein-Wiener models
idDeadZoneCreate a dead-zone nonlinearity estimator object
idPolynomial1DClass representing single-variable polynomial nonlinear estimator for Hammerstein-Wiener models
idPiecewiseLinearPiecewise-linear nonlinearity estimator object
idPiecewiseConstantPiecewise-constant nonlinearity estimator object (Da R2025a)
idSaturationCreate a saturation nonlinearity estimator object
idSigmoidNetworkSigmoid network function for nonlinear ARX and Hammerstein-Wiener models
idUnitGainSpecify absence of nonlinearities for specific input or output channels in Hammerstein-Wiener models
idWaveletNetworkWavelet network function for nonlinear ARX and Hammerstein-Wiener models
idGaussianProcessGaussian process regression mapping function for nonlinear ARX and Hammerstein-Wiener models (requires Statistics and Machine Learning Toolbox) (Da R2021b)
idNeuralNetworkMultilayer neural network mapping function for nonlinear ARX models and Hammerstein-Wiener models (requires Statistics and Machine Learning Toolbox or Deep Learning Toolbox) (Da R2023b)
evaluateEvaluate output values of idnlarx or idnlhw mapping object array for given set of input values
simSimulate response of identified model
simOptionsOption set for sim
compareCompare identified model output with measured output
compareOptionsOption set for compare
nlhwPlotPlot input and output nonlinearity, and linear responses of Hammerstein-Wiener model (Da R2023a)
evaluateEvaluate output values of idnlarx or idnlhw mapping object array for given set of input values
idnlhw/findopCompute operating point for Hammerstein-Wiener model
findopOptionsOption set for findop
idnlhw/operspecConstruct operating point specification object for idnlhw model
idnlhw/linearizeLinearize Hammerstein-Wiener model
linappLinear approximation of nonlinear ARX and Hammerstein-Wiener models for given input

Blocchi

espandi tutto

Hammerstein-Wiener ModelSimulate Hammerstein-Wiener model in Simulink software
Iddata SinkEsportare i dati della simulazione come oggetto iddata nel workspace di MATLAB
Iddata SourceImport time-domain data stored in iddata object in MATLAB workspace

Argomenti

Esempi in primo piano

A Tutorial on Identification of Nonlinear ARX and Hammerstein-Wiener Models

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

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.

Hammerstein-Wiener Model of SI Engine Torque Dynamics

Hammerstein-Wiener Model of SI Engine Torque Dynamics

Describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a Hammerstein-Wiener 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 Hammerstein-Wiener models, see Hammerstein-Wiener Models.