These two methods are coupled in such a way that HDMR addresses the global variation in the functional space using a set of component functions and the fine aberrations are interpolated by utilizing Kriging, performing as a two level approximation.
The component functions of the FHDMR are determined by using the component functions of the HDMR.
Also an object model of the component functions' structure, which allows to create component functions' structure complex is presented.
This allows superposition in time in addition to the superposition of component functions given by linearity.
It is observed that significantly less number of component functions are retained.
The method involves response surface generation of HDMR component functions, and Monte Carlo Simulation.
To reduce sampling effort, the RS-HDMR component functions are approximately represented by orthonormal polynomials.
The individual RS-HDMR component functions have a direct statistical correlation interpretation.
In our algorithm the component functions of the model are fit independently, in parallel; a simple iteration yields convergence to the optimal generalized additive model.
A unified algorithm, endowed with two distinct ranking schemes for grading component functions, was created for their numerical implementation.
From the range of involved component functions, we first discuss selected automatic mechanisms that control basic adaptational changes.
