SOME PROCEDURES MODIFICATION OF DATA ANALYSIS

UDC 519.23

A.A.Moiseev

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 do 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.

Keywords: data analysis, genetic optimization, stochastic search, crossing, mutation, potential functions, clasterization, perceptron, classifier, learning, fuzzy control.

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