Sequence of neural networks has been applied to high accuracy regression in 3D as data representation in form z = f(x,y). The first term of this series of networks estimates the values of the dependent variable as it is usual, while the second term estimates the error of the first network, the third term estimates the error of the second network and so on. Assuming that the relative error of every network in this sequence is less than 100 %, the sum of the estimated values converges to the values to be estimated, therefore the estimation error can be reduced very significantly and effectively. To illustrate this method the geoid of Hungary was estimated via RBF type network. The computations were carried out with the symbolic - numeric integrated system Mathematica.
In this case study a fully symbolic design and modeling method are presented for blood glucose control of diabetic patients under intensive care using Mathematica. The analysis is based on a modified two-compartment model proposed by Bergman et al. (2). The applied feedback control law decoupling even the nonlinear model leads to a fully symbolic solution of the closed loop equations. The effectivity of the applied symbolic procedures being mostly built-in the new version of Control System Professional Suite (CSPS) Application of Mathematica have been demonstrated for controller design in case of a glucose control for treatment of diabetes mellitus and also presented for a numerical situation described in Juhász (8). The results are in good agreement with the earlier presented symbolic-numeric analysis by Benyó et al. (1).