Paper Titles

Coordination Control Strategy for Active and Reactive Power of DFIG Based on Exact Feedback Linearization under Grid Fault Condition
p.335

Co-Sol-Gel Preparation of SiO₂-Doped TiO₂ on Adsorption and Photocatalytic Degradation of Methyl Orange
p.345

Cutting Tool Wear Monitoring Based on Wavelet Denoising and Fractal Theory
p.349

Data Compression in Automatic Ultrasonic Inspection Based on Lifting Scheme
p.353

Degree Dominance Interval Relation-Based RSM in Intuitionistic Fuzzy Decision Systems
p.357

DEM Simulation of the Particle Mixing in Spouted Bed of Three Solids Differing in Diameter
p.362

Description Logic Based Objects and Space Relations Representation
p.366

Design and Implementation of a Flexible Interline CCD Pixel Binning Method
p.373

Design and Validation of a Simulation-Based Modular Planning and Scheduling System of Semiconductor Fabrication Facilities
p.378

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 48-49Degree Dominance Interval Relation-Based RSM in...

Degree Dominance Interval Relation-Based RSM in Intuitionistic Fuzzy Decision Systems

Article Preview

Abstract:

By introducing a degree dominance relation to dominance interval intuitionistic fuzzy decision systems, we establish a degree dominance interval rough set model (RSM), which is mainly based on replacing the indiscernibility relation in classical rough set theory with the degree dominance interval relation. To simplify knowledge representation and extract some nontrivial simpler degree dominance interval intuitionistic fuzzy decision rules, we propose two attribute reductions of the degree dominance interval intuitionistic fuzzy decision systems that eliminate the redundant condition attributes that are not essential from the viewpoint of degree dominance interval intuitionistic fuzzy decision rules.

You might also be interested in these eBooks

Measuring Technology and Mechatronics Automation

Info:

Periodical:

Applied Mechanics and Materials (Volumes 48-49)

Pages:

357-361

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.48-49.357

Citation:

Cite this paper

Online since:

February 2011

Authors:

Bing Huang

Keywords:

Degree Dominance Interval Relation, Intuitionistic Fuzzy Decision System, Reduction, Rough Set Model

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Z. Pawlak, Rough sets, International Journal of Information and Computer Sciences 11 (1982) 341-356.

[2] C. Z. Wang, D. G. Chen, A short note on some properties of rough groups, Computers & Mathematics with Applications 59(1) (2010) 431-436.

DOI: 10.1016/j.camwa.2009.06.024

[3] J. Y. Shyng, H. M. Shieh, G. H. Tzeng, An integration method combining Rough Set Theory with formal concept analysis for personal investment portfolios, Knowledge-Based Systems 23(6) (2010) 586-597.

DOI: 10.1016/j.knosys.2010.04.003

[4] G. Xie, J. L. Zhang, K.K. Lai, Lean Yu, Variable precision rough set for group decision-making: An application, Approximation Reasoning 49(2) (2008) 331-343.

DOI: 10.1016/j.ijar.2007.04.005

[5] S. Greco, B. Matarazzo, R. Slowinski, Rough sets theory for multi-criteria decision analysis. European Journal of Operational Research 129 (2001) 1-47.

DOI: 10.1016/s0377-2217(00)00167-3

[6] S. Greco, B. Matarazzo, R. Slowinski, Rough sets methodology for sorting problems in presence of multiple attributes and criteria, European Journal of Operational Research 138 (2002) 247-259.

DOI: 10.1016/s0377-2217(01)00244-2

[7] R. Susmaga, R. Slowinski, S. Greto, B. Matarazzo, Grneration of reducts and rules in multi-attribute and multi-criteria classiﬁcation. Control and Cybernetics 29(2000) 969-988.

[8] P. Fortemps, S. Greco, R. Slowinski, Multicriteria decision support using rules that represent rough-graded preference relations, European Journal of Operational Research 188 (2008) 206-223.

DOI: 10.1016/j.ejor.2007.03.036

[9] W. Kotlowski, K. Dembczynski, S. Greco, R. Slowinski, Stochastic dominance-based rough set model for ordinal classiﬁcation, Information Sciences 178 (2008) 4019-4037.

DOI: 10.1016/j.ins.2008.06.013

[10] L.Y. Zhai, L.P. Khoo, Z.W. Zhong, A dominance-based rough set approach to Kansei Engineering in product development, Expert Systems with Applications 36 (2009) 393-402.

DOI: 10.1016/j.eswa.2007.09.041

[11] J. J. H. Liou, G. H. Tzeng, A Dominance-based rough set approach to customer behavior in the airline market. Information Sciences 180(11) (2010) 2230-2238.

DOI: 10.1016/j.ins.2010.01.025

[12] Q. Zhang, Z. P. Fan, D. H. Pan, A ranking approach for interval numbers in uncertain multiple attribute decision making problems. Systems Engineering-Theory and Practice 19(5) (1999) 129-133.

[13] J. Wu, D. S. Huang, A Review on ranking methods of interval numbers, Systems Engineering 22(8) (2004) 1-4.

[14] Z. S. Xu, Q. L. Da, Possibility degree method for ranking interval numbers and its application, Journal of Systems Engineering Engineering 18(1) (2003) 67-70.

[15] Y. Leung, M. M. Fischer, W. Z. Wu, Ju-Sheng Mi, A rough set approach for the discovery of classiﬁcation rules in interval-valued information systems, International Journal of Approximate Reasoning 47(2) (2008) 233-246.

DOI: 10.1016/j.ijar.2007.05.001

[16] Y. H. Qian, J. Y. Liang. Chuangyin Dang, Interval ordered information systems, Computers and Mathematics with Applications 56(8) (2008) 1994-(2009).

DOI: 10.1016/j.camwa.2008.04.021

[17] M. Chen, D. Q. Miao, Interval set clustering, Expert Systems with Applications, 2010(in press).

[18] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20(1986) 87-96.

DOI: 10.1016/s0165-0114(86)80034-3