REGULARIZING CALCULATIONS OF THE OUTPUT ENTROPY OF A NEURAL NETWORK "BIOMETRICS-CODE" CONVERTER THOUGH MULTIPLICATION OF A SMALL SAMPLE OF ORIGINAL DATA
Background. The aim of the paper is to regularize calculations of the entropy of long codes with dependent bits on a small testing sample. Materials and methods. The algorithm is based on predicting the probability of occurrence of rare events using the hypothesis of normal distribution of Hamming distances. It is suggested to abandon calculations of mathematical expectation and standard deviation in small samples, and proposed to multiply the original data by adding mutations obtained from a pseudorandom noise generator to them. Limitations to the mutation noise generator's amplitude are given. Results. It is shown that the transition to the Hamming distances' space leads to a logarithmic compression of the initial alphabet's size of the output states spectrum of a “biometrics-code” converter's molecule. This ultimately allows rapid testing of converters through calculation of their entropy. Conclusions. The procedure of regularization of calculations proposed in the article makes it possible to obtain the same accuracy of evaluating the entropy of a neural network converter on a sample of 21 experiments as the accuracy of calculations provided by standard computational procedures in accordance with GOST R 52633.3 on a sample of 2100 experiments. There is a 100-fold increase in the stability of computations.