scholarly journals Hierarchical Fuzzy Rule-Base System for MultiAgent Route Choice

10.5772/6277 ◽  
2008 ◽  
Author(s):  
Habib M. ◽  
Ilhem Kallel ◽  
Adel M.
Keyword(s):  
Route Choice ◽  
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

scholarly journals Prediction of Health Risk Potential Using Fuzzy Rule Base System for Indoor Work Environmental Condition in Dyeing and Printing House

2020 ◽  
Author(s):  
Dharmendra Jariwala ◽  
Robin A. Christian ◽  
Namrata D. Jariwala
Keyword(s):  
Health Risk ◽  
Work Environment ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Environmental Condition ◽  
Fuzzy Rule Base ◽  
Textile Dyeing ◽  
Very High ◽  
Environment Condition

Abstract The physical work environment in any industry is dynamic and unpredictable. It highly influencing to the health, comfort and well being of the occupants. Work environmental condition can be evaluated by measuring four basic comfort parameters, which include thermal comfort, acoustic comfort, visual comfort and air change inside the working area. These parameters are the major contributing factors to the health of workers. Usage of heat and water in the different processes of textile dyeing and printing industry make the work environment hot and humid. Due to high thermal stress a wide range of disease and complications has been observed from mild disorder to heatstroke. Also, the high level of noise and the poor lighting condition has been impacting on the stress, absenteeism, turnover, production and output quality in the industry. Improper ventilation in the working area will affect the dispersion and dilution of the pollutants generated due to the processes. The parameters considered for evaluating comfort levels include wet bulb globe temperature index, illumination, noise and air changes in this study. Fuzzy rule base system approach had been used to predict worker’s health risk associated due to the impact of the work environment condition. The modeling process has been carried out with the help of MATLAB (R2014a) fuzzy tool box. Triangular membership function had been used for the input parameters. Linguistic variables were finalized by the expert opinion. Total 144 fuzzy rules had been developed based on the linguistic variable. Output obtain is in the terms of crisp value [0,1] is divided into four linguistic term low, moderate, high and very high. The study reveals that work environment condition in the textile dyeing and printing mills at various sections of the industry fall in the category of a high and very high risk condition because of prevailing poor work environmental condition at various locations. Thus, the workers working in these sections having very high potential to get the diseases related to a hot and humid environment.

Enhancing the ORCA framework with a new Fuzzy Rule Base System implementation compatible with the JFML library

2021 ◽  
Author(s):  
Francisco Javier Rodriguez-Lozano ◽  
David Guijo-Rubio ◽  
Pedro Antonio Gutierrez ◽  
Jose Manuel Soto-Hidalgo ◽  
Juan Carlos Gamez-Granados
Keyword(s):  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
System Implementation ◽  
Fuzzy Rule Base
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