Abstract
The object of research is the process of selecting a synergistically determined pair for the elements of complex systems in the design, manufacture or repair. One of the most problematic places in the selection is the need to numerically evaluate the result of combining the elements, taking into account the explicit, additive properties of elements and hidden manifestations of the pair that are unusual for the elements alone (emergence). Lack of accounting for emergence can significantly distort the apparent picture of the processes taking place in systems, which makes many existing models of such processes inadequate. During the research, methods of extracting information from arrays known, hidden for direct observation were used. In particular, four-layer hidden Markov models with an additional hidden layer were used. The models were trained by the Baum-Welch method, adapted to work with an additional layer. As training samples used data obtained as a result of statistical processing of information available for object monitoring, expert assessments, as well as data obtained in the world's computer networks. The test of the method and model on real medical and technical objects confirms their clinical and technical effectiveness. In particular, thanks to this in the medical industry: in the medical industry, the incidence of thromboembolism of the branches of the pulmonary artery and deep veins of the thigh and lower leg are decreased by 65 %; frequency of postoperative bleeding is decreased by 43 %; by 36 % the total number of drug-related medicines aimed at correcting the blood coagulation system is decreased. In the technical field, the test results confirm the increase in the service life of rubber-metal shock absorbers by 14.5 %. This is due to the fact that the proposed method has a number of features, in particular, for the first time in its evaluation of emergence a four-layer hidden Markov model is used. The results obtained in the work make it possible to propose a general scheme of an intellectual decision support system in the selection of a synergistically determined pair of elements for complex systems of various purposes.
Highlights
In many applications of human activity, there are situa tions when to some object it is necessary to select a pair from a finite set of homogeneous objects
Creation and implementation of DSS use the method of evaluation and ranking system effects of combination of pairs of elements in an uncertain environment, built on the four-layer hidden Markov models
An emergent model based on a four-layer hidden Markov model is developed
Summary
In many applications of human activity, there are situa tions when to some object it is necessary to select a pair from a finite set of homogeneous objects. The most difficult is the requirement to take into account the total systemic effect of using two or more elements. The most convincing example of this phenomenon is the unexpected sharp negative side effect from the additional prescription of some separate «innocent» drugs to some patient. From this follows the urgent need not just to pick up a couple, longing for the obvious characteristics (there is not enough heat release from 30 kg of uranium-235, – take 60 kg!), and a careful analysis of the various hidden circumstances of this association, so as not to run into an atomic explosion (critical mass 235U – 50 kg). The task of pair matching is simultaneously tasks of image recognition and optimization problems, since the image is sought in some sense the best!
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