Abstract

In this paper we give results of applying two different variants of the Group Method of Data Handling (GMDH), a slightly modified basic (GMDH) and the so-called heuristic-free GMDH, to black-box modeling of dynamic systems. On the basis of observed input-output data, the GMDH net (a kind of neural network) is trained to reveal the "relevant" inputs with their time lags; besides this "data mining" (or more precisely "dependency modeling") aspect, the trained GMDH provides an output value for any concrete "relevant" input. The approach is tested on a number of application examples; a synthetic one and two real- world applications are considered in this paper. The (very computation intensive) heuristic- free approach shows the better performance, which justifies the employed parallel and/or distributed processing. Our parallel GMDH implementation allows flexible experimentation with various experiment parameters, such as o.a. different selection criteria. This facilitates finding the GMDH configurations with optimal performance.