Probabilistic-constrained fuzzy logic for situation modeling

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 lo...

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Veröffentlicht in:2009 IEEE International Conference on Fuzzy Systems S. 860 - 865
Hauptverfasser: Jinhua Xiong, Jianping Fan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2009
Schlagworte:
Calculus
Computer architecture
Context modeling
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Merging
Probabilistic logic
Probability
Uncertainty
ISBN:9781424435968, 142443596X
ISSN:1098-7584
Online-Zugang:Volltext
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Zusammenfassung: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.
ISBN:9781424435968
142443596X
ISSN:1098-7584
DOI:10.1109/FUZZY.2009.5277232