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INTELLIGENT SYSTEMS FOR DECISION SUPPORT
by
Dongrui Wu
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
May 2009
Copyright 2009 Dongrui Wu
Object Description
| Title | Intelligent systems for decision support |
| Author | Wu, Dongrui |
| Author email | dongruiw@usc.edu; drwuusc@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2009-03-27 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-04-27 |
| Advisor (committee chair) | Mendel, Jerry M. |
| Advisor (committee member) |
Kuo. C.-C. Jay Ershaghi, Iraj |
| Abstract | 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.; 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. |
| Keyword | intelligent systems; multi-criteria decision-making; fuzzy logic; information fusion; linguistic uncertainty |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2126 |
| Rights | Wu, Dongrui |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Wu-2847 |
| Archival file | uscthesesreloadpub_Volume26/etd-Wu-2847.pdf |
Description
| Title | Page 1 |
| Full text | INTELLIGENT SYSTEMS FOR DECISION SUPPORT by Dongrui Wu A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) May 2009 Copyright 2009 Dongrui Wu |
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