Application of possibilistic fuzzy regression for technology watch


Dereli T., DURMUŞOĞLU A.

Journal of Intelligent and Fuzzy Systems, cilt.21, sa.5, ss.353-363, 2010 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Özet
  • Cilt numarası: 21 Sayı: 5
  • Basım Tarihi: 2010
  • Doi Numarası: 10.3233/ifs-2010-0467
  • Dergi Adı: Journal of Intelligent and Fuzzy Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.353-363
  • Anahtar Kelimeler: fuzzy linear regression, Technology watch, trend extraction
  • Samsun Üniversitesi Adresli: Hayır

Özet

Identification and assessment of technological advances have been vital for companies to keep their competitive position or to gain new capabilities for the competition. In this content, Technology Watch Systems (TWS) have been tools of systematic analysis of technology developments that outputs regarding the technological opportunities and threats could be easily interpreted by an analyst. Among several TWS's, Patent Alert System (PAS) has been a recently developed one which enables users to set or configure alert(s) for the trend changes in a certain technology area of requested sector. Data of associated patent counts is retrieved by extended markup language (XML) mechanism located in PAS, and then an internal alert triggering mechanism is used to search for trend changes on the associated data. This internal alert triggering mechanism is a kind of modified linear regression which sets a newer trend line once a certain amount of deviation (threshold) has risen. Although alerts, indicating the direction of technological changes, provide supportive information to the analysts, extracted trend lines have been narrowed by a strict line where possible deviations have not been reflected. However, deviations in techno-systems are known to occur as the consequence of the vagueness coming from the nature of the system. Therefore, in this work, alert triggering mechanism of PAS is reconsidered using "possibilistic linear fuzzy regression". Results yielded better and promising outcomes for the reconsidered algorithm. © 2010-IOS Press and the authors. All rights reserved.