Paper Info
Title
Probabilistic-constrained fuzzy logic for situation modeling
Abstract
How to model situation user-friendly and precisely is a key issue for situation-aware applications. Fuzzy logic is an effective approach to model situation, but one obstacle is how to select the suitable operators between different fuzzy sets. One possibility is to combine the merit of both Fuzzy logic and Probability logic. The paper first introduces a set of constraints on conventional fuzzy logic and its operations, to setup a unified framework so as to combine the merits of the above two approaches. Such probabilistic-constrained fuzzy logic can be used in situation-aware applications. The paper then focuses on how to derive new fuzzy concepts from basic fuzzy partition, and how to compute the relationship between such derived and basic fuzzy concepts according to the probability constraints, which is different from the conventional ones.
Year
DOI
Venue
2009
10.1109/FUZZY.2009.5277232
FUZZ-IEEE
Keywords
Field
DocType
situation modeling,fuzzy logic,probabilistic-constrained fuzzy logic,model situation,probability logic,new fuzzy concept,different fuzzy set,conventional fuzzy logic,basic fuzzy partition,basic fuzzy concept,situation-aware application,uncertainty,fuzzy set,probabilistic logic,fuzzy sets,probability,ubiquitous computing,situation awareness,fuzzy set theory,computational modeling
Fuzzy electronics,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Theoretical computer science,Artificial intelligence,Fuzzy associative matrix,Fuzzy number,Type-2 fuzzy sets and systems,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Jinhua Xiong1828.60
Jianping Fan22677192.33