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Artificial Intelligence and Decision Making, 2017, Issue 4, Pages 95–108 (Mi iipr269)  

Decision support methods

Development of a conceptual model of artificial immune system prediction of the drift parameters of the onboard equipment

V. A. Smirnov, D. V. Smirnov

JSC "NPC "Akvamarin", St. Petersburg
Abstract: The problem of forecasting the drift of onboard equipment parameters is considered in the article. The purpose of this work is the formation of basic requirements, principles of construction, functioning and development of the conceptual model of the artificial immune system (IIS) for predicting the drift of critical parameters of an onboard automated control system (OACS) the flying machine (FM). Specificity of time series and scope is determined, biological models and heuristic methods for the proposed approach to forecasting are substantiated and selected. The comparison of the applied biological terms with their analogues from the subject domain is given. Proposed: a formal description and a conceptual model of IIS, structure of antigen, antibody and memory cells, a set of indicators of the quality of forecasting. The IIS model considered in the work has prospects for the successful application at the enterprises of the rocket and space industry in the creation of intelligent decision support systems (ISPPR) for the acceptance control of complex technical systems.
Keywords: prognostication, onboard automated control system, aftificial immune systems, modified clonal selection algorithm, conclusion on precedents, spline approximation.
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: V. A. Smirnov, D. V. Smirnov, “Development of a conceptual model of artificial immune system prediction of the drift parameters of the onboard equipment”, Artificial Intelligence and Decision Making, 2017, no. 4, 95–108
Citation in format AMSBIB
\Bibitem{SmiSmi17}
\by V.~A.~Smirnov, D.~V.~Smirnov
\paper Development of a conceptual model of artificial immune system prediction of the drift parameters of the onboard equipment
\jour Artificial Intelligence and Decision Making
\yr 2017
\issue 4
\pages 95--108
\mathnet{http://mi.mathnet.ru/iipr269}
\elib{https://elibrary.ru/item.asp?id=30771447}
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