A new design of Mamdani complex fuzzy inference system for multiattribute decision making problems

Ganeshsree Selvachandran, Shio Gai Quek, Luong Thi Hong Lan, Le Hoang Son, Nguyen Long Giang, Weiping Ding, Mohamed Abdel-Basset, Victor Hugo C. De Albuquerque

Research output: Contribution to journalArticleResearchpeer-review

51 Citations (Scopus)

Abstract

This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations.

Original languageEnglish
Pages (from-to)716-730
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Volume29
Issue number4
DOIs
Publication statusPublished - Apr 2021
Externally publishedYes

Keywords

  • Complex fuzzy inference system (CFIS)
  • complex fuzzy logic (CFL)
  • decision making
  • Mamdani fuzzy inference system (FIS)

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