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

被引：140

作者：

Choi, Byung-In ^{[1
]}

Rhee, Frank Chung-Hoon ^{[1
]}

机构：

[1] Hanyang Univ, Computat Vis & Fuzzy Syst Lab, Dept Elect Engn, Ansan, Gyeonggi Do, South Korea

来源：

INFORMATION SCIENCES | 2009年 / 179卷 / 13期

关键词：

Fuzzy membership function generation; Interval type-2 fuzzy sets; Fuzzy C-means; Footprint of uncertainty; LOGIC SYSTEMS;

D O I：

10.1016/j.ins.2008.04.009

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

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.

引用

页码：2102 / 2122

页数：21

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