Modelos Hammerstein-Wiener

Conexión de sistemas dinámicos lineales con no linealidades estáticas como saturación y zonas muertas

Utilice modelos Hammerstein-Wiener para estimar no linealidades estáticas en un sistema por lo demás lineal. Puede utilizar la estructura Hammerstein-Wiener para capturar efectos no lineales físicos en sensores y actuadores que afectan a la entrada y salida de un sistema lineal, como zonas muertas y saturación. Para estimar modelos Hammerstein-Wiener, utilice la app System Identification o la función nlhw.

Apps

System IdentificationIdentificar modelos de sistemas dinámicos a partir de datos medidos.

Funciones

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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 (Desde 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) (Desde R2021b)
idNeuralNetworkMultilayer neural network mapping function for nonlinear ARX models and Hammerstein-Wiener models (requires Statistics and Machine Learning Toolbox or Deep Learning Toolbox) (Desde 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 (Desde 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

Bloques

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Hammerstein-Wiener ModelSimulate Hammerstein-Wiener model in Simulink software
Iddata SinkExportar los datos de simulación como objeto iddata al área de trabajo de MATLAB
Iddata SourceImport time-domain data stored in iddata object in MATLAB workspace

Temas

Ejemplos destacados

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.