Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A complete axiomatization for fuzzy functional and multivalued dependencies in fuzzy database relations
Date
2001-01-15
Author
Sozat, MI
Yazıcı, Adnan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
281
views
0
downloads
Cite This
This paper first introduces the formal definitions of fuzzy functional and multivalued dependencies which are given on the basis of the conformance values presented here. Second, the inference rules are listed after both fuzzy functional and multivalued dependencies are shown to be consistent, that is, they reduce to those of the classic functional and multivalued dependencies when crisp attributes are involved. Finally, the inference rules presented here are shown to be sound and complete for the family of functional and multivalued dependencies in fuzzy database relations.
Subject Keywords
Logic
,
Artificial Intelligence
URI
https://hdl.handle.net/11511/62451
Journal
FUZZY SETS AND SYSTEMS
DOI
https://doi.org/10.1016/s0165-0114(98)00152-3
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Stable controller design for T-S fuzzy systems based on Lie algebras
Banks, SP; Gurkan, E; Erkmen, İsmet (Elsevier BV, 2005-12-01)
In this paper, we study the stability of fuzzy control systems of Takagi-Sugeno-(T-S) type based on the classical theory of Lie algebras. T-S fuzzy systems are used to model nonlinear systems as a set of rules with consequents of the type x(t) = A(l)x (t) + B(l)u (t). We conduct the stability analysis of such T-S fuzzy models using the Lie algebra LA generated by the A(l) matrices of these subsystems for each rule in the rule base. We first develop our approach of stability analysis for a commuting algebra ...
AN EFFICIENT DATABASE TRANSITIVE CLOSURE ALGORITHM
Toroslu, İsmail Hakkı; HENSCHEN, L (Springer Science and Business Media LLC, 1994-05-01)
The integration of logic rules and relational databases has recently emerged as an important technique for developing knowledge management systems. An important class of logic rules utilized by these systems is the so-called transitive closure rules, the processing of which requires the computation of the transitive closure of database relations referenced by these rules. This article presents a new algorithm suitable for computing the transitive closure of very large database relations. This algorithm proc...
Improving reinforcement learning by using sequence trees
Girgin, Sertan; Polat, Faruk; Alhajj, Reda (Springer Science and Business Media LLC, 2010-12-01)
This paper proposes a novel approach to discover options in the form of stochastic conditionally terminating sequences; it shows how such sequences can be integrated into the reinforcement learning framework to improve the learning performance. The method utilizes stored histories of possible optimal policies and constructs a specialized tree structure during the learning process. The constructed tree facilitates the process of identifying frequently used action sequences together with states that are visit...
A binomial noised model for cluster validation
Toledano-Kitai, Dvora; Avros, Renata; Volkovich, Zeev; Weber, Gerhard Wilhelm; Yahalom, Orly (IOS Press, 2013-01-01)
Cluster validation is the task of estimating the quality of a given partition of a data set into clusters of similar objects. Normally, a clustering algorithm requires a desired number of clusters as a parameter. We consider the cluster validation problem of determining the optimal ("true") number of clusters. We adopt the stability testing approach, according to which, repeated applications of a given clustering algorithm provide similar results when the specified number of clusters is correct. To implemen...
A complete axiomatization for fuzzy functional and multivalued dependencies in fuzzy database relations
Yazıcı, Adnan (1996-09-11)
In this paper we first introduce a new definition for the conformance of tuples existing in a fuzzy database relation. Then we give the formal definitions of fuzzy functional and multivalued dependencies on the basis of the conformance values presented here. Secondly, we list the inference rules after showing that both fuzzy functional and multivalued dependencies are consistent, that is, they reduce to those of the classic functional and multivalued dependencies when crisp attributes are involved. Finally,...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. Sozat and A. Yazıcı, “A complete axiomatization for fuzzy functional and multivalued dependencies in fuzzy database relations,”
FUZZY SETS AND SYSTEMS
, pp. 161–181, 2001, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62451.