Intellectual analysis of the results of the cliometric monitoring
DOI:
https://doi.org/10.34121/1028-9763-2019-4-87–92Keywords:
Cliometric, GMDH, Monitoring, Clustering, Data Mining, кліометрія, МГУА, моніторинг, кластеризація, інтелектуальний аналіз данихAbstract
Abstract. In order to increase the adequacy of the results of clinical studies, information technology of multilevel intelligent monitoring is used in conjunction with expert estimates of simulation results. The results were interpreted in the framework of the classical concepts of historical science. This contributes to attracting to the analysis of sources of systems of artificial intelligence (knowledge bases, expert systems, cognitive computer models of understanding of the text, frame systems), in which the knowledge of historians is modeled. When forming an array of numerical characteristics of certain historical periods, there is a tendency towards increasing the proportion of historical sources created by the collective method. The application of intelligent monitoring technology allows automating the processing of historical data and improving the efficiency of research and the adequacy of the findings. The results of the application of the technology of multilevel intelligent monitoring for solving one of the problems of climmetry are presented. The problem of determining the similarity of historical periods was solved. The list of features describing historical periods is determined expertly. The length of the time interval is selected based on the results of processing the statistics. Numerical characteristics of the selected signs were formed during the same time intervals and formed a vector of signs for each of the historical periods. Vectors of signs of historical periods were subjected to clustering by the results of modeling. The model synthesis method was selected separately for the formation of each cluster based on the results of testing each of the algorithms for the synthesis of models of the monitoring intellectual system. In most cases, model clusterizers were built on a multi-row GMDH algorithm. The processes of the formation of the input data array, the synthesis of models and the determination of the effect of the signs are described. The feasibility of using a new method for determining the similarity of historical periods has been experimentally confirmed.References
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