Data Mining for the Tasks of Identifying the States of Objects Control in Conditions of Uncertainty
The article proposes the ways to improve information analysis conducted by national and regional government bodies for public security tasks. The proposed approach is based on the idea of possible adaptation of artificial neural networks for identifying critical states of subject areas in conditions of incomplete or partially invalid input data. The presented simulation experiment describes the particular steps for the preparation of initial data to study a neural network model based on the situation of partial randomization of reference vectors of signs. The validity of using neural network methods for identifying the control objects state is determined by a comparative analysis between the effectiveness of their use and traditionally used methods that are based on the establishment of a metric distance between the results of operational monitoring and the reference state of the control object.