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Intelligent systems for decision sup...

University of Southern California., Electrical Engineering.

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Intelligent systems for decision support.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Intelligent systems for decision support./
作者:	Wu, Dongrui.
面頁冊數:	311 p.
附註:	Adviser: Jerry M. Mendel.
Contained By:	Dissertation Abstracts International70-05B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3355362
ISBN:	9781109137385

Intelligent systems for decision support.
Wu, Dongrui.

Intelligent systems for decision support. - 311 p.

Adviser: Jerry M. Mendel.

Thesis (Ph.D.)--University of Southern California, 2009.

This research is focused on multi-criteria decision-making (MCDM) under uncertainties, especially linguistic uncertainties. This problem is very important because many times linguistic information, in addition to numerical information, is an essential input of decision-making. Linguistic information is usually uncertain, and it is necessary to incorporate and propagate this uncertainty during the decision-making process because uncertainty means risk.

ISBN: 9781109137385Subjects--Topical Terms:

626642
Computer Science.

Intelligent systems for decision support.
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520 $a This research is focused on multi-criteria decision-making (MCDM) under uncertainties, especially linguistic uncertainties. This problem is very important because many times linguistic information, in addition to numerical information, is an essential input of decision-making. Linguistic information is usually uncertain, and it is necessary to incorporate and propagate this uncertainty during the decision-making process because uncertainty means risk.
520 $a MCDM problems can be classified into two categories: (1) multi-attribute decision-making (MADM), which selects the best alternative(s) from a group of candidates using multiple criteria, and (2) multi-objective decision-making (MODM), which optimizes conflicting objective functions under constraints. Perceptual Computer, an architecture for computing with words, is implemented in this dissertation for both categories. For MADM, we consider the most general case that the weights for and the inputs to the criteria are a mixture of numbers, intervals, type-1 fuzzy sets and/or words modeled by interval type-2 fuzzy sets. Novel weighted averages are proposed to aggregate this diverse and uncertain information so that the overall performance of each alternative can be computed and ranked. For MODM, we consider how to represent the dynamics of a process (objective function) by IF-THEN rules and then how to perform reasoning based on these rules, i.e., to compute the objective function for new linguistic inputs. Two approaches for extracting IF-THEN rules are proposed: (1) linguistic summarization to extract rules from data, and (2) knowledge mining to extract rules through survey. Applications are shown for all techniques proposed in this dissertation.
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