How the Software Computes Nonlinear ARX Model Output

This topic describes how the software evaluates the output of nonlinearity estimators and uses this output to compute the response of a nonlinear ARX model.

Evaluating Nonlinearities

Evaluating the predicted output of a nonlinearity for a specific regressor value x requires that you first extract the nonlinearity F and regressors from the model:

F = m.Nonlinearity;
x = getreg(m,'all',data) % computes regressors

Evaluate F(x):

y = evaluate(F,x)

where x is a row vector of regressor values.

You can also evaluate predicted output values at multiple time instants by evaluating F for several regressor vectors simultaneously:

y = evaluate(F,[x1;x2;x3])

Simulation and Prediction of Sigmoid Network

This example shows how the software computes the simulated and predicted output of a nonlinear ARX model as a result of evaluating the output of its nonlinearity estimator for given regressor values.

 Estimating and Exploring a Nonlinear ARX Model

 Prediction of Output

 Simulation of Output

 Nonlinearity Evaluation

See Also

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