Cognitive Neural Network Foresight to Forecast Scientific and Technological Development of the State. (17th October 2015)

• Record Type: Journal Article
• Authors: Gorbachev, Sergey; Syryamkin, Maksim; Syryamkin, Vladimir; Vaganova, Elena
• Is Part Of: Procedia--social and behavioral sciences. Volume 206(2015)
• Page Start: 378
• Page End: 382
• Publication Date: 2015-10-17
• Subjects: Neural network approach -- neurofuzzy decision tree -- neural network -- database mining -- cognitive technologies -- sixth wave of innovation -- techno-economic development; Social sciences -- Periodicals; Social sciences -- Congresses; Arts -- Periodicals; Arts -- Congresses; Humanities -- Periodicals; Humanities -- Congresses; 300
• DOI: 10.1016/j.sbspro.2015.10.069
• Languages: English
• ISSNs: 1877-0428

Abstract

Abstract: In the paper, we focus on the possibilities of neural network approach to assess the potential of Russia in achieving indicators of the sixth wave of innovation. The purpose of this study is to develop evolutionary algorithms and tools for neurofuzzy output of science-based assessments of the analysis and formation of scientific and technological areas and the List of critical technologies of the Russian Federation. In this paper, in the framework of the Foresight study, we used the results of expert seminars, as well as methods of remote study. Database mining tools are a class of hybrid systems of computational intelligence. The systems operate on the basis of the principles that are significantly different from data processing methods in conventional artificial neural networks relating to cognitive ("smart") technology. Such hybrid neurofuzzy systems possess the most powerful cognitive capacity (modelling of sensation and perception; pattern recognition, learning and memorizing of patterns in order to identify the knowledge of the data). Such systems have wider range of application than other methods of synthesis of fuzzy sets and neural networks. The effect of identifying patterns in the neural network model provides comprehensive heterogeneous parameters that are not sufficient when applied separately. Trained mental model will calculate the weight factors and identify diagnostic decision rules "If, then", in which certain indicators carry the weight load of the solution. The developed methods have found practical implementation in the development of the report (essay) in the Ministry of Education and Science of the Russian Federation within the framework of works on long-term forecast of the most important areas of scientific and technological development of the Russian Federation for the period until 2030.