The forecasting model based on fuzzy novel ν-support vector machine

Qi Wu, Chun Hung Roberts Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


This paper presents a new version of fuzzy support vector machine to forecast multi-dimension fuzzy sample. By combining the triangular fuzzy theory with the modified ν-support vector machine, the fuzzy novel ν-support vector machine (FNν-SVM) is proposed, whose constraint conditions are less than those of the standard Fν-SVM by one, is proved to satisfy the structure risk minimum rule under the condition of probability. Moreover, there is no parameter b in the regression function of the FNν-SVM. To seek the optimal parameters of the FNν-SVM, particle swarm optimization is also proposed to optimize the unknown parameters of the FNν-SVM. The results of the application in sale forecasts confirm the feasibility and the validity of the FNν-SVM model. Compared with the traditional model, the FNν-SVM method requires fewer samples and has better forecasting precision.
Original languageEnglish
Pages (from-to)12028-12034
Number of pages7
JournalExpert Systems with Applications
Issue number10
Publication statusPublished - 15 Sept 2011


  • Fuzzy ν-support vector machine
  • Particle swarm optimization
  • Sale forecasts
  • Triangular fuzzy number

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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