The complex fuzzy system forecasting model based on fuzzy SVM with triangular fuzzy number input and output

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

This paper presents a new version of fuzzy support vector machine to forecast the nonlinear fuzzy system with multi-dimensional input variables. The input and output variables of the proposed model are described as triangular fuzzy numbers. Then by integrating the triangular fuzzy theory and v-support vector regression machine, the triangular fuzzy v-support vector machine (TFv-SVM) is proposed. To seek the optimal parameters of TFv-SVM, particle swarm optimization is also applied to optimize parameters of TFv-SVM. A forecasting method based on TFv-SVRM and PSO are put forward. The results of the application in sale system forecasts confirm the feasibility and the validity of the forecasting method. Compared with the traditional model, TFv-SVM method requires fewer samples and has better forecasting precision.
Original languageEnglish
Pages (from-to)12085-12093
Number of pages9
JournalExpert Systems with Applications
Volume38
Issue number10
DOIs
Publication statusPublished - 15 Sep 2011

Keywords

  • Fuzzy system forecasting
  • Fuzzy v-support vector machine
  • Particle swarm optimization
  • Wavelet kernel function

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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