nonlinear arx models -凯发k8网页登录

nonlinear behavior modeled using dynamic networks such as sigmoid and wavelet

use nonlinear arx models to represent nonlinearities in your system using dynamic nonlinearity estimators such as wavelet networks, tree-partitioning, and sigmoid networks. in the toolbox, these models are represented as objects. you can estimate nonlinear arx models in the system identification app, or at the command line using the command.

apps

system identification

identify models of dynamic systems from measured data

functions

model estimation

	nonlinear arx model
	estimate parameters of nonlinear arx model
	option set for `nlarx`
	detect nonlinearity in estimation data
	set or randomize initial parameter values
	obtain model parameters and associated uncertainty data
	modify values of model parameters

model regressors

	specify linear regressor for nonlinear arx model (since r2021a)
	specify polynomial regressor for nonlinear arx model (since r2021a)
	specify periodic regressor for nonlinear arx model (since r2022a)
	specify custom regressor for nonlinear arx model (since r2021a)
	regressor expressions and numerical values in nonlinear arx model
	(not recommended) powers and products of standard regressors
	(not recommended) custom regressor for nonlinear arx models
	(not recommended) add custom regressors to nonlinear arx model

nonlinear and linear mapping objects

	wavelet network function for nonlinear arx and hammerstein-wiener models
	sigmoid network function for nonlinear arx and hammerstein-wiener models
	tree-partitioned nonlinear function for nonlinear arx models
	custom network function for nonlinear arx and hammerstein-wiener models
	linear mapping object for nonlinear arx models
	gaussian process regression mapping function for nonlinear arx and hammerstein-wiener models (requires statistics and machine learning toolbox) (since r2021b)
	decision tree ensemble mapping function for nonlinear arx models (requires statistics and machine learning toolbox) (since r2021b)
	support vector machine regression mapping function for nonlinear arx models (requires statistics and machine learning toolbox) (since r2022a)
	multilayer neural network mapping function for nonlinear arx models and hammerstein-wiener models (requires statistics and machine learning toolbox or deep learning toolbox) (since r2023b)
	(not recommended) multilayer feedforward neural network mapping function for nonlinear arx models (requires deep learning toolbox)
	evaluate output values of `idnlarx` or `idnlhw` mapping object array for given set of input values

simulation, prediction, and validation

	simulate response of identified model
	option set for `sim`
	predict identified model k-step-ahead output
	option set for `predict`
	compare identified model output with measured output
	option set for `compare`
	forecast identified model output
	option set for `forecast`
	plot nonlinearity of nonlinear arx model (since r2023a)
	evaluate output values of `idnlarx` or `idnlhw` mapping object array for given set of input values
	get input/output delay information for `idnlarx` model structure

linearization

	compute operating point for nonlinear arx model
	option set for `findop`
	construct operating point specification object for `idnlarx` model
	linearize nonlinear arx model
	linear approximation of nonlinear arx and hammerstein-wiener models for given input

blocks

simulation

	simulate nonlinear arx model in simulink software
	export simulation data as `iddata` object to matlab workspace
	import time-domain data stored in `iddata` object in matlab workspace

topics

understand the structure of a nonlinear arx model.
choose from sigmoid, wavelet, tree partition, linear, neural, and custom network nonlinearities.
specify the nonlinear arx structure, and configure the estimation algorithm.
plot model nonlinearities, analyze residuals, and simulate and predict model output.
simulate, predict, and forecast model output, linearize nonlinear arx models, and import estimated models into the simulink^® software.
choose the approach for computing linear approximations, compute operating points for linearization, and linearize your model.
how the software evaluates the output of nonlinearity estimators and uses this output to compute the model response.

featured examples

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.