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Intelligent systems for decision support.

机译：提供决策支持的智能系统。

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摘要

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.
机译：这项研究专注于不确定性（尤其是语言不确定性）下的多标准决策（MCDM）。这个问题非常重要，因为除数字信息外，许多语言信息也是决策的重要输入。语言信息通常是不确定的，在决策过程中必须合并并传播这种不确定性，因为不确定性意味着风险。MCDM问题可分为两类：（1）多属性决策（MADM），它使用多个标准从一组候选者中选择最佳选择，以及（2）多目标决策（MODM），它在约束条件下优化冲突的目标函数。本文针对这两种类别，实现了感知计算机，一种用于单词计算的架构。对于MADM，我们考虑最一般的情况，即标准的权重和输入是数字，区间，第1类模糊集和/或由区间2类模糊集建模的单词的混合。提出了新颖的加权平均值来汇总这种多样且不确定的信息，以便可以计算和排名每个替代方案的整体性能。对于MODM，我们考虑如何通过IF-THEN规则表示流程的动态（目标函数），然后如何基于这些规则执行推理，即为新的语言输入计算目标函数。提出了两种提取IF-THEN规则的方法：（1）语言汇总，从数据中提取规则；（2）知识挖掘，通过调查提取规则。展示了本文提出的所有技术的应用。

著录项

  • 作者

    Wu, Dongrui.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Engineering System Science.;Computer Science.;Operations Research.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 311 p.
  • 总页数 311
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 系统科学;运筹学;自动化技术、计算机技术;
  • 关键词

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