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Decoupling multivariate functions using a nonparametric filtered tensor decomposition
Multivariate functions emerge naturally in a wide variety of data-driven models.
Applying Polynomial Decoupling Methods to the Polynomial NARX Model
That said, a major disadvantage with the polynomial NARX model is that the number of parameters increases rapidly with increasing polynomial order.
A frequency domain approach for local module identification in dynamic networks
In the second step, the estimated non-parametric FRF of the target module is smoothed using a parametric frequency domain estimator with the estimated variance from the previous step as the non-parametric noise model.
Decoupling multivariate functions using a non-parametric Filtered CPD approach
Usually a generic basis function expansion is used, e. g. a polynomial basis, and the parameters of the function are tuned given the data.
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