東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Interval-based uncertain reasoning.

Wang, Jian.

Linked to FindBook

Google Book

Amazon

博客來

Interval-based uncertain reasoning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Interval-based uncertain reasoning./
Author:	Wang, Jian.
Description:	61 p.
Notes:	Source: Masters Abstracts International, Volume: 37-03, page: 0969.
Contained By:	Masters Abstracts International37-03.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ33462
ISBN:	0612334627

Interval-based uncertain reasoning.
Wang, Jian.

Interval-based uncertain reasoning. - 61 p.

Source: Masters Abstracts International, Volume: 37-03, page: 0969.

Thesis (M.Sc.)--Lakehead University (Canada), 1997.

This thesis examines three interval based uncertain reasoning approaches: reasoning under interval constraints, reasoning using necessity and possibility functions, and reasoning with rough set theory. In all these approaches, intervals are used to characterize the uncertainty involved in a reasoning process when the available information is insufficient for single-valued truth evaluation functions. Approaches using interval constraints can be applied to both interval fuzzy sets and interval probabilities. The notion of interval triangular norms, or interval t-norms for short, is introduced and studied in both numeric and non-numeric settings. Algorithms for computing interval t-norms are proposed. Basic issues on the use of t-norms for approximate reasoning with interval fuzzy sets are studied. Inference rules for reasoning under interval constraints are investigated. In the second approach, a pair of necessity and possibility functions is used to bound the fuzzy truth values of propositions. Inference in this case is to narrow the gap between the pair of the functions. Inference rules are derived from the properties of necessity and possibility functions. The theory of rough sets is used to approximate truth values of propositions and to explore modal structures in many-valued logic. It offers an uncertain reasoning method complementary to the other two.

ISBN: 0612334627Subjects--Topical Terms:

626642
Computer Science.

Interval-based uncertain reasoning.
LDR:02192nmm 2200277 4500 001 1815236
005 20060623071201.5
008 130610s1997 eng d
020 $a 0612334627
035 $a (UnM)AAIMQ33462
035 $a AAIMQ33462
040 $a UnM $c UnM
100 1 $a Wang, Jian. $3 794935
245 1 0 $a Interval-based uncertain reasoning.
300 $a 61 p.
500 $a Source: Masters Abstracts International, Volume: 37-03, page: 0969.
500 $a Adviser: Yi Yu Yao.
502 $a Thesis (M.Sc.)--Lakehead University (Canada), 1997.
520 $a This thesis examines three interval based uncertain reasoning approaches: reasoning under interval constraints, reasoning using necessity and possibility functions, and reasoning with rough set theory. In all these approaches, intervals are used to characterize the uncertainty involved in a reasoning process when the available information is insufficient for single-valued truth evaluation functions. Approaches using interval constraints can be applied to both interval fuzzy sets and interval probabilities. The notion of interval triangular norms, or interval t-norms for short, is introduced and studied in both numeric and non-numeric settings. Algorithms for computing interval t-norms are proposed. Basic issues on the use of t-norms for approximate reasoning with interval fuzzy sets are studied. Inference rules for reasoning under interval constraints are investigated. In the second approach, a pair of necessity and possibility functions is used to bound the fuzzy truth values of propositions. Inference in this case is to narrow the gap between the pair of the functions. Inference rules are derived from the properties of necessity and possibility functions. The theory of rough sets is used to approximate truth values of propositions and to explore modal structures in many-valued logic. It offers an uncertain reasoning method complementary to the other two.
590 $a School code: 1099.
650 4 $a Computer Science. $3 626642
650 4 $a Artificial Intelligence. $3 769149
690 $a 0984
690 $a 0800
710 2 0 $a Lakehead University (Canada). $3 1018578
773 0 $t Masters Abstracts International $g 37-03.
790 1 0 $a Yao, Yi Yu, $e advisor
790 $a 1099
791 $a M.Sc.
792 $a 1997
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ33462