Interval type-2 fuzzy membership function generation methods for pattern recognition

  • Authors:

  • Byung-In Choi;Frank Chung-Hoon Rhee

  • Affiliations:

  • Computational Vision and Fuzzy Systems Laboratory, Department of Electronic Engineering, Hanyang University, 1271 Sa 1-Dong Sangnok-Gu, Ansan-Si, Gyeonggi-Do, Republic of Korea;Computational Vision and Fuzzy Systems Laboratory, Department of Electronic Engineering, Hanyang University, 1271 Sa 1-Dong Sangnok-Gu, Ansan-Si, Gyeonggi-Do, Republic of Korea

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2009

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Abstract

Type-2 fuzzy sets (T2 FSs) have been shown to manage uncertainty more effectively than T1 fuzzy sets (T1 FSs) in several areas of engineering [4,6-12,15-18,21-27,30]. However, computing with T2 FSs can require undesirably large amount of computations since it involves numerous embedded T2 FSs. To reduce the complexity, interval type-2 fuzzy sets (IT2 FSs) can be used, since the secondary memberships are all equal to one [21]. In this paper, three novel interval type-2 fuzzy membership function (IT2 FMF) generation methods are proposed. The methods are based on heuristics, histograms, and interval type-2 fuzzy C-means. The performance of the methods is evaluated by applying them to back-propagation neural networks (BPNNs). Experimental results for several data sets are given to show the effectiveness of the proposed membership assignments.