Hammerstein-Wiener 모델

포화, 불감대와 같은 정적 비선형성을 선형 동적 시스템에 연결

정적 비선형성을 제외하면 선형 시스템이 되는 시스템에서 Hammerstein-Wiener 모델로 정적 비선형성을 추정합니다. 이들 모델은 툴박스에서 idnlhw 객체로 표현됩니다. System Identification 앱 또는 명령줄에서 nlhw 명령을 사용하여 Hammerstein-Wiener 모델을 추정할 수 있습니다.

앱

System Identification

측정된 데이터에서 동적 시스템의 모델 식별하기

함수

모두 확장

모델 추정

`idnlhw`	Hammerstein-Wiener Model
`nlhw`	Estimate Hammerstein-Wiener model
`nlhwOptions`	Option set for `nlhw`
`init`	Set or randomize initial parameter values
`getpvec`	Obtain model parameters and associated uncertainty data
`setpvec`	Modify values of model parameters

비선형성 표현

`idCustomNetwork`	Custom network function for nonlinear ARX and Hammerstein-Wiener models
`idDeadZone`	Create a dead-zone nonlinearity estimator object
`idPolynomial1D`	Class representing single-variable polynomial nonlinear estimator for Hammerstein-Wiener models
`idPiecewiseLinear`	Create a piecewise-linear nonlinearity estimator object
`idSaturation`	Create a saturation nonlinearity estimator object
`idSigmoidNetwork`	Sigmoid network function for nonlinear ARX and Hammerstein-Wiener models
`idUnitGain`	Specify absence of nonlinearities for specific input or output channels in Hammerstein-Wiener models
`idWaveletNetwork`	Wavelet network function for nonlinear ARX and Hammerstein-Wiener models
`idGaussianProcess`	Gaussian process regression mapping function for nonlinear ARX and Hammerstein-Wiener models (requires Statistics and Machine Learning Toolbox) (R2021b 이후)
`idNeuralNetwork`	Multilayer neural network mapping function for nonlinear ARX models and Hammerstein-Wiener models (requires Statistics and Machine Learning Toolbox or Deep Learning Toolbox) (R2023b 이후)
`evaluate`	Evaluate output values of `idnlarx` or `idnlhw` mapping object array for given set of input values

시뮬레이션 및 검증

`sim`	Simulate response of identified model
`simOptions`	Option set for `sim`
`compare`	Compare identified model output with measured output
`compareOptions`	Option set for `compare`
`nlhwPlot`	Plot input and output nonlinearity, and linear responses of Hammerstein-Wiener model (R2023a 이후)
`evaluate`	Evaluate output values of `idnlarx` or `idnlhw` mapping object array for given set of input values

선형화

`idnlhw/findop`	Compute operating point for Hammerstein-Wiener model
`findopOptions`	Option set for `findop`
`idnlhw/operspec`	Construct operating point specification object for `idnlhw` model
`idnlhw/linearize`	Linearize Hammerstein-Wiener model
`linapp`	Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input

블록

모두 확장

시뮬레이션

Hammerstein-Wiener Model	Simulate Hammerstein-Wiener model in Simulink software
Iddata Sink	시뮬레이션 데이터를 `iddata` 객체로 MATLAB 작업 공간으로 내보내기
Iddata Source	Import time-domain data stored in `iddata` object in MATLAB workspace

도움말 항목

What Are Hammerstein-Wiener Models?
Understand the structure of Hammerstein-Wiener models.
Available Nonlinearity Estimators for Hammerstein-Wiener Models
Choose from a set of scalar nonlinearity estimators that you can use for both input and output estimators in Hammerstein-Wiener models.
Identifying Hammerstein-Wiener Models

Specify the Hammerstein-Wiener model structure, and configure the estimation algorithm.
Validating Hammerstein-Wiener Models
Plot model nonlinearities, analyze residuals, and simulate model output.
Using Hammerstein-Wiener Models
Simulate and predict model output, linearize Hammerstein-Wiener 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 Hammerstein-Wiener Model Output
How the software evaluates the output of nonlinearity estimators and uses this output to compute the model response.