Fuzzy Variable Optimal Selection Model with Multiple Attribute Intervals and Its Application

2008 ◽  
Author(s):  
Ran-hang Zhao ◽  
Xiao-li Liu ◽  
Wen-yan Chen
Keyword(s):  
Fuzzy Variable ◽  
Selection Model ◽  
Optimal Selection ◽  
Multiple Attribute

Cooperative selection of movements: The optimal selection model

1996 ◽  
Vol 58 (4) ◽  
pp. 254-273 ◽  
Author(s):  
Jonathan Vaughan ◽  
David A. Rosenbaum ◽  
Frederick J. Diedrich ◽  
Cathleen M. Moore
Keyword(s):  
Selection Model ◽  
Optimal Selection ◽  
Selection Of

Adaptive Inter CU Depth Decision for HEVC Using Optimal Selection Model and Encoding Parameters

2017 ◽  
Vol 63 (3) ◽  
pp. 535-546 ◽  
Author(s):  
Yue Li ◽  
Gaobo Yang ◽  
Yapei Zhu ◽  
Xiangling Ding ◽  
Xingming Sun
Keyword(s):  
Selection Model ◽  
Optimal Selection

scholarly journals Reliability Evaluation Optimal Selection Model of Component-Based System

2011 ◽  
Vol 04 (07) ◽  
pp. 433-441 ◽  
Author(s):  
Guo Yong ◽  
Wan Tian Tian ◽  
Ma Pei Jun ◽  
Su Xiao Hong
Keyword(s):  
Reliability Evaluation ◽  
Selection Model ◽  
Optimal Selection

An Optimal Selection Model of the Satellite Lurk Orbit

2012 ◽  
Vol 249-250 ◽  
pp. 270-273
Author(s):  
Xiao Kun Zhang ◽  
Ying Kui Gong ◽  
Jiang Hua Qu
Keyword(s):  
Selection Model ◽  
Maintenance Cost ◽  
Optimal Selection ◽  
Satisfaction Degree ◽  
Selection Processes ◽  
Special Cases ◽  
Orbit Maintenance ◽  
Space Target ◽  
Orbit Selection

An optimal selection model of the lurk orbit on which a satellite will park waiting for instructions aiming at a non-cooperative space target is presented. The model uses factors including orbit maneuver satisfaction degree in special cases and orbit utilization value in ordinary uses taking into account orbit maintenance cost, orbit availability and the resulting elusiveness. These factors are derived from dynamics characteristics of the space target orbit and the optional lurk orbits. The model is employed to select the optimal lurk orbit from several optional orbits for a satellite taking aim at a non-cooperative space target so as to secure ordinary utilization and effective respond when receiving instruction to perform tasks concerning the space target. In the presented work, lurk orbit selection processes in an application example are analyzed using the model. It is seen that the optimal selection model effectively solves lurk orbit selection

SOME GEOMETRIC AGGREGATION FUNCTIONS AND THEIR APPLICATION TO DYNAMIC MULTIPLE ATTRIBUTE DECISION MAKING IN THE INTUITIONISTIC FUZZY SETTING

2009 ◽  
Vol 17 (02) ◽  
pp. 179-196 ◽  
Author(s):  
G. W. WEI
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Membership Function ◽  
Fuzzy Set ◽  
Fuzzy Numbers ◽  
Fuzzy Variable ◽  
Multiple Attribute Decision Making ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute

The intuitionistic fuzzy set (IFS) characterized by a membership function and a non-membership function, was introduced by [K. Atanassov, "Intuitionistic fuzzy sets", Fuzzy Sets and Systems20 (1986) 87–96] as a generalization of Zadeh' fuzzy set [L. A. Zadeh, "Fuzzy sets", Information and Control8 (1965) 338–356] to deal with fuzziness and uncertainty. In this paper, the dynamic multiple attribute decision making (DMADM) problems with intuitionistic fuzzy information are investigated. The notions of intuitionistic fuzzy variable and uncertain intuitionistic fuzzy variable are defined, and two new aggregation operators called dynamic intuitionistic fuzzy weighted geometric (DIFWG) operator and uncertain dynamic intuitionistic fuzzy weighted geometric (UDIFWG) operator are proposed. Moreover, a procedure based on the DIFWG and IFWG operators is developed to solve the dynamic intuitionistic fuzzy multiple attribute decision making problems where all the decision information about attribute values takes the form of intuitionistic fuzzy numbers collected at different periods, and a procedure based on the UDIFWG and IIWG operators is developed for uncertain dynamic intuitionistic fuzzy multiple attribute decision making problems under interval uncertainty in which all the decision information about attribute values takes the form of interval-valued intuitionistic fuzzy numbers collected at different periods. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

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