The System Identification Toolbox™ software provides three types of nonlinear model structures:
The toolbox refers to Nonlinear ARX and Hammerstein-Wiener collectively as "nonlinear black box" models. You can configure these models in a variety of ways to represent various behavior using nonlinear functions such as wavelet networks, tree partitions, piece-wise linear functions, polynomials, saturation and dead zones.
The nonlinear grey-box models lets you to estimate coefficients of nonlinear differential equations.
Nonlinear ARX models extend the linear ARX models to the nonlinear case and have this structure:
y(t) = f(y(t - 1), ..., y(t - na), u(t - nk), ..., u(t -nk -nb + 1))
where the function f depends on a finite number of previous inputs u and outputs y. na is the number of past output terms and nb is the number of past input terms used to predict the current output. nk is the delay from the input to the output, specified as the number of samples.
Use this model to represent nonlinear extensions of linear models. This structure allows you to model complex nonlinear behavior using flexible nonlinear functions, such as wavelet and sigmoid networks. Typically, you use nonlinear ARX models as black-box structures. The nonlinear function of the nonlinear ARX model is a flexible nonlinearity estimator with parameters that need not have physical significance.
Hammerstein-Wiener models describe dynamic systems using one or two static nonlinear blocks in series with a linear block. The linear block is a discrete transfer function and represents the dynamic component of the model.
You can use the Hammerstein-Wiener structure to capture physical nonlinear effects in sensors and actuators that affect the input and output of a linear system, such as dead zones and saturation. Alternatively, use Hammerstein-Wiener structures as black box structures that do not represent physical insight into system processes.
Nonlinear state-space models have this representation:
where F and H can have any parameterization.
A nonlinear ordinary differential equation of high order can be represented as a set
of first order equations. You use the
idnlgrey object to specify the
structures of such models based on physical insight about your system. The
parameters of such models typically have physical interpretations. Use this model to
represent nonlinear ODEs with unknown parameters.
For more information about estimating nonlinear state-space models, see Grey-Box Model Estimation.