RETRACTED ARTICLE: Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique
Miloš Milovančević,
Dušan Marković (),
Vlastimir Nikolić and
Igor Mladenović
Additional contact information
Miloš Milovančević: University of Niš
Dušan Marković: University of Niš
Vlastimir Nikolić: University of Niš
Igor Mladenović: University of Niš
Quality & Quantity: International Journal of Methodology, 2017, vol. 51, issue 3, No 15, 1207-1216
Abstract:
Abstract Number of patents may be developed on the basis on different natural and science and technological factors. Number of patents prediction based on the different factors in many countries is analyzed in this investigation. These factors represent natural and science resources. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for the number of patents prediction. Five inputs are considered: research and development (R&D) resources, natural resources, quality of academic institutions, quality of collaboration with the private sector and quality of education. As the ANFIS output, number of patents is considered. The ANFIS process for variable selection is also implemented in order to detect the predominant factors affecting the prediction of number of patents. Results show that the R&D is the most influential factor for the number of patents prediction.
Keywords: ANFIS; Prediction; Number of patents; R&D; Innovation; Education (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s11135-016-0326-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0326-1
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135
DOI: 10.1007/s11135-016-0326-1
Access Statistics for this article
Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi
More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().