Y.E. Shishkin1,2, A.V. Skatkov1
1 Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33
2 Institute of Natural and Technical Systems, RF, Sevastopol, Lenin St., 28
The article proposes an intelligent decision support system for choosing a statistical criterion for detecting divergence when analyzing monitoring data of complex objects and environments, taking into account the power and sensitivity of the criterion as well as the presence of risks. A scheme for selecting the parametric difference criterion depending on the size and number of samples is presented. A component of intelligent information technology has been developed that selects the optimal parametric criterion for detecting differences in monitoring data based on automatic selection of scenarios.
The basis of the intellectualization of the decision support system for choosing criteria for distinguishing samples of monitoring data from complex objects and environments is to enter decision points that enable decision makers to determine priorities: the sensitivity or the probability of false positives, in the case when there is no Pareto-optimal solution.In the theory of decision making, the greatest complexity is caused by the formation of an evaluation matrix, the process of which is still not fully formalized and does not have a specific decision algorithm. Therefore, it was proposed to carry out the formation of an evaluation matrix based on the information scenario technology. The decision maker has some freedom in the choice of scenarios, while under the scenario this freedom is limited, this restriction can be removed by expanding the list of scenarios. It is possible to facilitate the selection of scenarios by introducing intelligent solutions based on artificial intelligence technologies and adaptation mechanisms.
To illustrate the intellectual approach in the framework of information technology, three basic decision-making scenarios under uncertainty are considered: a minimax approach with relative estimates, direct voting and risk minimization.
Keywords: intellectualization, environment monitoring, parametric criteria, mathematical modeling, complex systems, data mining, decision support.
LIST OF REFERENCES:
- Chimitova E. V., Vedernikova M. A., Galanova N. S. Nonparametric consent criteria in problems of checking the adequacy of reliability models based on censored data // Bulletin of Tomsk state University. Management, computer engineering and Informatics. 2013. № 4 (25). P. 115–124.
- Kudlaev E. M., Orlov A. I. Probabilistic and statistical methods of research in the works of A. N. Kolmogorov / / factory laboratory. Diagnostics of materials. 2003. Vol. 69. № 5. P. 55–61.
- Shishkin Y.E. Big Data visualization in decision making // Science in Progress: tesas. Everything is fine. scientific-practical Conf. undergraduates and postgraduates. Novosibirsk, October 20, 2016 Novosibirsk: NSTU, 2016. P. 203–205.
- Grzybowski, A. M. Analysis of three or more independent groups of quantitative data // Human ecology. 2008. № 3. P. 50–58.
- Novikov D. A. Statistical methods in pedagogical research (typical cases). Moscow: MZ-Press, 2004. P. 67.
- Shishkin Yu. E., Skatkov A.V. Quality Metrics for evaluating and predicting critical States / / Quality and life. 2019. № 1 (21). P. 61–66.
- Larichev O. I. Properties of decision-making methods in multi-criteria problems of individual choice / / Automatics and telemechanics. 2002. № 2. P. 146–158.
- Shishkin Yu. E., Skatkov A.V. Increasing the reliability of risk assessments in monitoring processes for General distributions // Monitoring systems of environment. 2019. № 1 (35). P. 41–47. DOI: 10.33075/2220-5861-2019-1-41-47
- Podinovski V. V., Nogin V. D. Pareto optimal solutions of multicriteria problems. М., 2007. P. 256.
- Grishko A. K., Kochegarov I. I., Lysenko A.V. Multi-Criteria selection of the optimal variant of a complex technical system based on interval analysis of weakly structured information // Measurement. Monitoring. Management. Control. 2017. № 3 (21). P. 97–107.
- Bashmakov A. I., Bashmakov I. A. Intellectual information technologies: Computer science at the technical University. М., 2005. P. 302.
- Skatkov A.V., Bryukhovetsky A. A., Shishkin Yu. E. Development of intellectual technology for detecting anomalies in the water area of Sevastopol // Monitoring systems of environment. 2019. № 1 (35). P. 27–34. DOI: 10.33075/2220-5861-2019-1-27-34
- Lemeshko B. Yu., Lemeshko S. B. On the stability and power of criteria for checking the homogeneity of averages // Measurement technology. 2008. № 9. P. 23–28.
- Arkhipova N. I., Kononov D. A., kulba V. V. The Problem of choosing a scenario for monitoring the safe functioning of complex systems // Problems of security management of complex systems: proceedings of the XVII international conference. Russian Academy of Sciences / edited By N. I. Arkhipova, V. V. Kulba. 2009. P. 148–151.
- Massel L. V., Galper V. I. Development of multi-agent systems for distributed solution of the power problems with the use of agent-based scenarios // Proceedings of Tomsk Polytechnic University. Engineering of geo-resources. 2015. Vol. 326. № 5. P. 45–53.