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Interval Type-2 Intuitionistic Fuzzy Logic Systems - A Comparative Evaluation

  • Imo EyohEmail author
  • Robert John
  • Geert De Maere
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 853)

Abstract

Several fuzzy modeling techniques have been employed for handling uncertainties in data. This study presents a comparative evaluation of a new class of interval type-2 fuzzy logic system (IT2FLS) namely: interval type-2 intuitionistic fuzzy logic system (IT2IFLS) of Takagi-Sugeno-Kang (TSK)-type with classical IT2FLS and its type-1 variant (IFLS). Simulations are conducted using a real-world gas compression system (GCS) dataset. Study shows that the performance of the proposed framework with membership functions (MFs) and non-membership functions (NMFs) that are each intervals is superior to classical IT2FLS with only MFs (upper and lower) and IFLS with MFs and NMFs that are not intervals in this problem domain.

Keywords

Interval type-2 intuitionistic fuzzy logic systems Membership functions Non-membership functions Decoupled extended Kalman filter 

Notes

Acknowledgement

This research work was supported by the Government of Nigeria under the Tertiary Education Trust Fund (TETFund).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Automated Scheduling, Optimisation and Planning (ASAP) and Laboratory for Uncertainty in Data and Decision Making Research GroupsUniversity of NottinghamNottinghamUK
  2. 2.Automated Scheduling, Optimisation and Planning (ASAP) Research GroupUniversity of NottinghamNottinghamUK

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