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Principal component parts as a new factor of innovative competitiveness

Zabolotskii A.A. Institute of Economics and Industrial Engineering, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russian Federation ( ieie@inbox.ru )

Journal: Finance and Credit, #46, 2016

Subject The article addresses component parts and their impact on the aggregating innovation effect as constituents of innovation.
Objectives The aim of the paper is to study the structure of analytical systems of search for and integration of component parts, and the advantage of their application in Russia.
Methods The paper rests on evaluation of data and experience accumulated from the cooperation with international corporations, and employs data visualization for comparison and analysis.
Results I adjust the cluster model of analytical innovation systems development in Russia. The model is based on searching for production chain participants through tenders, and this precludes from accumulating the necessary innovation capacity. The paper offers to create innovative systems that rely on a wide and multistage selection of elementary components with a deep level of integration, rather than a simple assembly of finished components. It identifies major classes of elementary components and their impact on final innovative products.
Conclusions The paper updates the process of competitive advantages formation at the level of elementary components, and highlights key problems in the formation of components of the country's innovative competitiveness when developing the import substitution programs.


Modeling of transformation of technological development using two-dimensional neural self-organizing maps

Zabolotskii A.A. Institute of Economics and Industrial Engineering, Siberian Branch of RAS (IEIE SB RAS), Akademgorodok, Novosibirsk, Russian Federation ( ieie@inbox.ru )

Journal: Finance and Credit, #1, 2019

Subject This article discusses the issues of technological development, namely its limits, influencing the economic and innovation growth.
Objectives The article aims to study and prove the existence of the limits of technological development.
Methods The research uses an innovative method of forecasting on the basis of weight vectors of neural self-organizing maps (SOM).
Results The article explores the transformation of investment and the changing structure of innovative progress that has led to this transformation. It shows two main factors, namely the limit of scientific breakthrough and the technological limit that influence the transformation. The article also models the process of generating scientific breakthroughs using a two-dimensional self-organizing map neural network (MATLAB neural clustering tool), and on the basis of these results, it makes predictions up to 2050.
Conclusions and Relevance The neural network weight map can be used to model and forecast evolutionary systems, as well as for accurate forecasting of technologies that may appear in the future, or can show the exhaustion of the potential of innovative development for these input component sets and technologies.


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