抄録
In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the "standard" regression.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 606-617 |
| ページ数 | 12 |
| ジャーナル | European Journal of Operational Research |
| 巻 | 210 |
| 発行部数 | 3 |
| DOI | |
| 出版物ステータス | Published - 2011 5 1 |
Fingerprint
ASJC Scopus subject areas
- Management Science and Operations Research
- Modelling and Simulation
- Information Systems and Management
これを引用
Real-time fuzzy regression analysis : A convex hull approach. / Ramli, Azizul Azhar; Watada, Junzo; Pedrycz, Witold.
：: European Journal of Operational Research, 巻 210, 番号 3, 01.05.2011, p. 606-617.研究成果: Article
}
TY - JOUR
T1 - Real-time fuzzy regression analysis
T2 - A convex hull approach
AU - Ramli, Azizul Azhar
AU - Watada, Junzo
AU - Pedrycz, Witold
PY - 2011/5/1
Y1 - 2011/5/1
N2 - In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the "standard" regression.
AB - In this study, we present an enhancement of fuzzy regression analysis with regard to its aspect of real-time processing. Let us recall that fuzzy regression generalizes the concept of classical (numeric) regression in the sense of bringing additional capabilities that allow the model to deal with fuzzy (granular) data. We show that a convex hull method provides a useful vehicle to reduce computing time, which becomes of particular relevance in case of real-time data analysis. Our objective is to develop an efficient real-time fuzzy regression analysis based on the use of convex hull, specifically a Beneath-Beyond algorithm. In this algorithm, the re-construction of convex hull edges depends on incoming vertices while a re-computing procedure can be realized in real-time. We demonstrate the use of the developed enhancement to application to unit performance assessment and air pollution data. An important role of convex hull is contrasted with the limitations of linear programming used in the "standard" regression.
KW - Convex hull
KW - Fuzzy regression analysis
KW - Fuzzy set
KW - Linear programming
KW - Regression analysis
UR - http://www.scopus.com/inward/record.url?scp=79151469745&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79151469745&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2010.10.007
DO - 10.1016/j.ejor.2010.10.007
M3 - Article
AN - SCOPUS:79151469745
VL - 210
SP - 606
EP - 617
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 3
ER -