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Multicriteria decision analysis evaluates multiple conflicting criteria in decision making, but conflicting criteria are typical in evaluating options. As the existing ordering operations involved in multicriteria decision making cannot easily be implemented with intervals, we assume that scalar representative values with intervals can effectively avoid this issue. To deal with interval-valued criteria, we propose a generalized golden rule representative value approach, which involves the sigmoid function of backpropagation neural networks to tune parameters. Our approach considers the uncertainties and side effects of the interval variables to improve individual scalar representative values. Based on numerical examples, we address the effectiveness of the proposed approach, and we provide a specific application concerning multicriteria decision making with interval criteria satisfaction.

Item Type:	Refereed Article
Keywords:	comparison system, golden rule representative value, interval valued, multicriteria decision function, sigmoid function, uncertainty, uzzy rule
Research Division:	Information and Computing Sciences
Research Group:	Machine learning
Research Field:	Neural networks
Objective Division:	Defence
Objective Group:	Defence
Objective Field:	Intelligence, surveillance and space
UTAS Author:	Kang, BH (Professor Byeong Kang)
UTAS Author:	Cao, Z (Dr Zehong Cao)
ID Code:	133136
Year Published:	2019
Web of Science® Times Cited:	11
Deposited By:	Information and Communication Technology
Deposited On:	2019-06-13
Last Modified:	2020-05-18
Downloads:	23 View Download Statistics