Neural network approach to solving the problem of forecasting scientific and technological development of country

The article describes the method of identifying emerging innovations, ideas and technologies based on the study of intellectual property items of the major patent offices using neural network analysis of the most promising technologies and scientific-technological areas, capable of forming the sixth technological order. The expert survey with neural network data processing was conducted. The analysis of the lighting market has been conducted and patent information has been studied in order to determine the «technological peaks» in the industry. The study suggests that the LED technologies in Russia have great potential

Keywords: neural network classification, technological structure, patent analysis, critical technologies

References

 

  1. Ahmetzhanova S. B., Marinova V. B., Tusupbekov M. B., 2012, Forsa’tnye metody issledovani’ v mirovoi praktike [World-wide Foresight research methods].  Economic Research Institute of the Republic of Kazakhstan [Online]. Available at: http://www.economy.kz/files/vse%20stati/5%20ahmet.pdf. [Accessed 1 June 2014]
  2. Syryamkin V. I., (ed.), 2012, Kognitivnye sistemy monitoringa i prognoza nauchno-tehnologicheskogo razvitiya gosudarstva [Cognitive systems of monitoring and forecasting of scientific and technological development of the country], Publishing House of Tomsk State Univestity, Tomsk.
  3. Forsait: obzor issledovanii i dostizhenii [Foresight: review of research and achievements], 2009, Expert portal of Higher School of Economics [Online]. Available at: http://opec.ru/1146450.html [Accessed 1 June 2014]
  4. Gan G. (2007) Data Clustering: Theory, Algorithms and Applications, Philadelphia: SIAM.
  5. Gorbachev S. V., Syryamkin V. I., Syryamkin M. V. Intellektual'nyi Forsait-prognoz prioritetov nauchno-tehnologicheskogo razvitiya gosudarstva [Intelligent Foresight of the scientific and technological development of the state], 2012, LAMBERT Academic Publishing, Saarbrucken.
  6. Zadeh L. A., 1978, Fuzzy Sets as a Basis for a Theory of Possibility, Fuzzy Sets and Systems, Vol.1, no. 1, p.3-28.
  7. Janikow C. Z., 1998, Fuzzy decision trees: Issues and methods, IEEE transactions on systems man and cybernetics part B-cybernetics, Vol. 28, no. 1, p. 1-14.
  8. Kohonen T.,1990, Self-Organizing Map, Proceedings of the IEEE, Vol. 78, no. 9, p. 1464-1480
  9. European Patent Organization [Online]. Available at: http://www.epo.org/index.html, [20 June 2014].
  10. Turkin A., 2009, Perspektivy primeneniya moshnyh svetodiodov Cree dlya osvesheniya [Prospects of application of high-power LEDs Cree for lighting], Novosti elektroniki, no. 9, [Online]. Available at: http://www.compeljournal.ru/enews/2009/9/7 [Accessed 1 June 2014]
  11. Davidenko Yu., 2004, Sovremennye svetodiody [Modern LED], Components and technologies, no. 6, [Online]. Available at: http://kit-e.ru/articles/led/2004_6_38.php, [Accessed 1 June 2014]
  12. Karvonen M., Kässi T., 2013,Patent citations as a tool for analyzing the early stages of convergence, Technological Forecasting and Social Change, Vol. 80, no. 6, July, p. 1094-1107.
  13. Tseng F.-M., Hsieh C.-H., Peng Y.-N., ChuY.-W., 2011, Using patent data to analyze trends and the technological strategies of the amorphous silicon thin-film solar cell industry, Technological Forecasting and Social Change, Vol. 78, no. 2, February, p. 332-345.
  14. Ernst H. (1997) The Use of Patent Data for Technological Forecasting: The Diffusion of CNC-Technology in the Machine Tool Industry, Small Business Economics, Vol. 9, no. 4, p. 361-381.
  15. Cavaller V. (2009) Scientometrics and patent bibliometrics in RUL analysis: A new approach to valuation of intangible assets, VINE, Vol. 39, no. 1, April, p. 80-91.

 

Authors