DocumentCode :
707652
Title :
Cognitive neural network modeling of the trajectory of global technical and economic development
Author :
Gorbachev, Sergey ; Syryamkin, Vladimir
Author_Institution :
Dept. of Innovative Technol., Nat. Res. Tomsk State Univ., Tomsk, Russia
fYear :
2015
fDate :
3-4 March 2015
Firstpage :
1
Lastpage :
6
Abstract :
The article considers the problem of measuring the level and rate of technical and economic development of countries in terms of technological change. Advantages of cognitive neural network approach to monitoring and quality analysis for integrating into a single model of economic, scientific-technological, innovative and other quantitative and qualitative components of growth, not amenable to traditional statistical analysis, with calculation of the forecast evaluation time of the reference trajectory of technical and economic development. Presents the results of the calculations. To improve the accuracy of the model trajectories are encouraged to use self-organizing Kohonen maps.
Keywords :
economics; self-organising feature maps; cognitive neural network modeling; economic development; self-organizing Kohonen maps; statistical analysis; technical development; technological change; Analytical models; Economic indicators; Mathematical model; Neural networks; Technological innovation; Trajectory; global modeling; indicators; innovation; neural networks; prediction; the trajectory of technical and economic development;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Computing and Information Processing (CCIP), 2015 International Conference on
Conference_Location :
Noida
Type :
conf
DOI :
10.1109/CCIP.2015.7100697
Filename :
7100697
Link To Document :
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