Instead of comparison of the statistical procedures and artificial neural nets work there is possibility to unite them into common twice-multilayered neuronet. Any good working regression or pattern recognition procedure can be considered as one neuron, output of which is one of the outputs pointed out in input data sample. You can repeat such procedure for all another variables pointed out in data sample. Such way you get first layer of neuronet with active neurons. The output variables should be added to variables of data sample. They are very effective secondary inputs for the neurons of the next layer. By repeating this operation you can construct second, third and so on, layers of neuronet. The number of layers should be increased until the error criterion decreases. This approach show principal advantage of neural nets. Full set of variables we can propose to a statistical procedure only one time, in twice-multilayered neuronets we can propose corrected full sets of variables at each layer.

Neuronets with active neurons allow to optimize the regression space: effective variables (factors) are chosen at each neuronet level. Regression analysis (in the case of accurate and long input data samples) or GMDH algorithms (in the case of noised or short data) should be used for one single active neuron in neuronet. Neuronet with collective of active neurons gives new possibility to generate and to select new combination of inputs. This can highly increase modeling accuracy with the help of regression area extension.