Automatic data analysis is most substantial component of computer – aided data processing. Along with classical procedures of statistical analysis – factor’s, dispersion’s, discriminates ones – it includes also some additional procedures not related directly with statistical analysis.
Particularly, these are procedures of genetic optimization, classification by means of starting sample clasterization, some method of perceptron adaptation on teaching sample, some procedures of fuzzy control. Now performed essential facilities to consolidate such procedures in form of united intellectual technology. In the framework of these facilities here considered some modifications of these procedures by means of their simplification – ideologically and practically. Performed some algorithms consideration of data analysis, that’s shown their base simplicity. Genetic optimization were transformed to two – step version of stochastic search, whose steps are preliminary mixing of primary search results (interpreted as crossing) and secondary stochastic search (interpreted as mutation). Potential function method allowed implementing the simple procedure of clasterization without any additional requirements to input sample. Learning algorithm of perceptron’s classifier was used the preliminary averaging in secondary neurons with any constant subtraction. Additional adaptive coefficients normalizing does it insufficient at maximization used as decisive function. Fuzzy control learning were developed that’s based on control transactions frequencies equalization at equidistant sample of input states.