Towards type-2 fuzzy rule base system for road choice

2015 ◽  
Author(s):  
Mariam Zouari ◽  
Sahar Cherif ◽  
Habib Kammoun ◽  
Hela Lajmi ◽  
Adel M. Alimi
Keyword(s):  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Fuzzy Rule Base

A Data-Adaptive Fuzzy Rule base System for Putamen Segmentation in Brain MR Images

2006 ◽  
Vol 12 (4) ◽  
pp. 431-441
Author(s):  
Tao Song ◽  
Mingxiong Huang ◽  
Roland R. Lee ◽  
Jamshidi Mo
Keyword(s):  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Mr Images ◽  
Adaptive Fuzzy ◽  
Fuzzy Rule Base ◽  
Brain Mr Images ◽  
Data Adaptive

Fuzzy inference system application for failure analyzing in automobile industry

2017 ◽  
Vol 34 (9) ◽  
pp. 1493-1507 ◽  
Author(s):  
Arash Geramian ◽  
Mohammad Reza Mehregan ◽  
Nima Garousi Mokhtarzadeh ◽  
Mohammadreza Hemmati
Keyword(s):  
Automobile Industry ◽  
Failure Modes ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Inference System ◽  
Content Type ◽  
Fuzzy Rule Base ◽  
Applied Method ◽  
Fuzzy Fmea

Purpose Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry. Design/methodology/approach Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran. Findings Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization. Practical implications This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system. Originality/value This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.

A novel fuzzy rule base system for pose independent faces detection

2011 ◽  
Vol 11 (2) ◽  
pp. 1801-1810 ◽  
Author(s):  
Payman Moallem ◽  
Bibi Somayeh Mousavi ◽  
S. Amirhassan Monadjemi
Keyword(s):  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Fuzzy Rule Base
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