Assessing E-waste recycling programs by developing preference selection index under interval type-2 fuzzy uncertainty

2016 ◽  
Author(s):  
V. Mohagheghi ◽  
S. M. Mousavi ◽  
A. Siadat
Keyword(s):  
Selection Index ◽  
Waste Recycling ◽  
Fuzzy Uncertainty ◽  
Interval Type

scholarly journals An Interval Type-2 Possibilistic C-Means Clustering Algorithm and Its Application

2021 ◽  
Vol 2132 (1) ◽  
pp. 012016
Author(s):  
Haihua Xing ◽  
Huannan Chen ◽  
Hongyan Lin ◽  
Xinghui Wu
Keyword(s):  
Image Segmentation ◽  
Fuzzy Sets ◽  
Clustering Algorithm ◽  
High Order ◽  
Data Sets ◽  
Fuzzy Uncertainty ◽  
Penalty Factor ◽  
Interval Type

Abstract In this paper, we aim at the fuzzy uncertainty caused by noise in pattern data. The advantages of PCM algorithm to deal with noise and interval type-2 fuzzy sets to deal with high-order uncertainties are used, respectively. An interval type-2 probability C-means clustering (IT2-PCM) based on penalty factor is proposed. The performance of the algorithm is evaluated by two sets of data sets and two groups of images segmentation experiments. The results show that IT2-PCM algorithm can assign proper membership degrees to clustering samples with noise, and it can detect noise points effectively, and it has good performance in image segmentation.

Influence of different fuzzy operators on analytical structure and variable gains of typical interval type-2 fuzzy PI controller

2020 ◽  
Vol 39 (3) ◽  
pp. 4319-4329
Author(s):  
Haibo Zhou ◽  
Chaolong Zhang ◽  
Shuaixia Tan ◽  
Yu Dai ◽  
Ji’an Duan ◽  
...  
Keyword(s):  
Pi Controller ◽  
Real Time Control ◽  
Fuzzy Operators ◽  
Fuzzy Operator ◽  
Analytical Structure ◽  
Product Operator ◽  
Pi Controllers ◽  
Fuzzy Pi Controller ◽  
Interval Type

The fuzzy operator is one of the most important elements affecting the control performance of interval type-2 (IT2) fuzzy proportional-integral (PI) controllers. At present, the most popular fuzzy operators are product fuzzy operator and min() operator. However, the influence of these two different types of fuzzy operators on the IT2 fuzzy PI controllers is not clear. In this research, by studying the derived analytical structure of an IT2 fuzzy PI controller using typical configurations, it is proved mathematically that the variable gains, i.e., proportional and integral gains of typical IT2 fuzzy PI controllers using the min() operator are smaller than those using the product operator. Moreover, the study highlights that unlike the controllers based on the product operator, the controllers based on the min() operator have a simple analytical structure but provide more control laws. Real-time control experiments on a linear motor validate the theoretical results.

Sign in / Sign up

Export Citation Format

Share Document