Interval type-2 fuzzy membership function generation methods for pattern recognition
- Authors
- Choi, Byung-In; Rhee, Frank Chung-Hoon
- Issue Date
- Jun-2009
- Publisher
- ELSEVIER SCIENCE INC
- Keywords
- Fuzzy membership function generation; Interval type-2 fuzzy sets; Fuzzy C-means; Footprint of uncertainty
- Citation
- INFORMATION SCIENCES, v.179, no.13, pp.2102 - 2122
- Indexed
- SCIE
SCOPUS
- Journal Title
- INFORMATION SCIENCES
- Volume
- 179
- Number
- 13
- Start Page
- 2102
- End Page
- 2122
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/41101
- DOI
- 10.1016/j.ins.2008.04.009
- ISSN
- 0020-0255
- 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. (C) 2008 Elsevier Inc. All rights reserved.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.