A practical inference method with several implicative gradual rules and a fuzzy input: one and two dimensions
Hazaël Jones,Didier Dubois,Serge Guillaume,B. Charnomordic +3 more
- 23 Jul 2007
- Vol. 11278, pp 1-6
TL;DR: A general approach to practical inference with gradual implicative rules and fuzzy inputs is presented, based on fuzzy output computation under an interval-valued input and the use of inferential independence.
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Abstract: A general approach to practical inference with gradual implicative rules and fuzzy inputs is presented. Gradual rules represent constraints restricting outputs of a fuzzy system for each input. They are tailored for interpolative reasoning. Our approach to inference relies on the use of inferential independence. It is based on fuzzy output computation under an interval-valued input. A double decomposition of fuzzy inputs is done in terms of alpha-cuts and in terms of a partitioning of these cuts according to areas where only a few rules apply. The case of one and two dimensional inputs is considered.
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Citations
Mining Frequent Gradual Itemsets from Large Databases
Lisa Di-Jorio,Anne Laurent,Maguelonne Teisseire +2 more
- 27 Aug 2009
TL;DR: This paper formally define gradual association rules and an original lattice-based approach and the GRITE algorithm is proposed for extracting gradual itemsets in an efficient manner for handling huge volumes of complex numerical data.
89
Fast extraction of gradual association rules: a heuristic based method
Lisa Di Jorio,Anne Laurent,Maguelonne Teisseire +2 more
- 28 Oct 2008
TL;DR: This paper applies a heuristic in order to extract tendencies, in the form of gradual association rules, on valued data and proposes an efficient heuristic, thus reducing combinatorial complexity and scalability.
•Proceedings Article
Gradual Rules: A Heuristic Based Method and Application to Outlier Extraction
Lisa Di Jorio,Anne Laurent,Maguelonne Teisseire +2 more
- 01 Jan 2009
TL;DR: This paper applies a heuristic in order to extract tendencies, in the form of gradual association rules, directly on valued data and proposes an efficient heuristic, thus reducing combinatorial complexity and scalability.
1
References
An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller
E.H. Mamdani,S. Assilian +1 more
TL;DR: Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy, and the control strategy set up linguistically proved to be far better than expected in its own right.
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Rough fuzzy sets and fuzzy rough sets
Didier Dubois,Henri Prade +1 more
TL;DR: It is argued that both notions of a rough set and a fuzzy set aim to different purposes, and it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems.
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What are fuzzy rules and how to use them
Didier Dubois,Henri Prade +1 more
TL;DR: A survey of different possible semantics for a fuzzy rule and shows how they can be captured in the framework of fuzzy set and possibility theory.
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•Book
Gradual rules and the approximation of control laws
Didier Dubois,Henri Prade,Michel Grabisch +2 more
- 03 Mar 1995
54
On generalized modus ponens with multiple rules and a residuated implication
Nehad N. Morsi,Aly A. Fahmy +1 more
TL;DR: It is shown that a multiple-rule, generalized modus ponens inference scheme, with an interpretation based on compositional rule of inference (CRI) and a residuated implication, is equivalent to a system that satisfies the "basic requirement for fuzzy reasoning", proposed by Turksen and Tian.
40
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