Fuzzy networks with rule base aggregation for selection of alternatives

Abdul Yaakob, Alexander Gegov, Siti Fatimah Abdul Rahman

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This paper introduces a novel extension of the Technique for Ordering of Preference by Similarity to Ideal Solution (TOPSIS) method. The method is based on aggregation of rules with different linguistic of the output of fuzzy networks to solve multi criteria decision-making problems whereby both benefit and cost criteria are presented as subsystems. Thus the decision maker evaluates the performance of each alternative for decision process and further observes the performance for both benefit and cost criteria. The aggregation sub-stage in a fuzzy system maps the fuzzy membership functions for all rules to an aggregated fuzzy membership function representing the overall output for the rules. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison to established approaches. To ensure practicality and effectiveness, the proposed method is further tested on portfolio selection problems. The ranking produced by the method is comparatively validated using Spearman rho rank correlation. The results show that the proposed method outperforms the existing TOPSIS approaches in term of ranking performance.
Original languageEnglish
Pages (from-to)123-144
Number of pages12
JournalFuzzy Sets and Systems
Early online date7 Jun 2017
Publication statusPublished - 15 Jun 2018


  • Fuzzy Networks
  • Selection Alternatives
  • Fuzzy sets
  • Interval type 2 fuzzy sets
  • Z-Numbers
  • Stock selection
  • Spearman rho


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