Symbolic Execution and Thresholding for Efficiently Tuning Fuzzy Logic Programs

2017 ◽  
pp. 131-147 ◽  
Author(s):  
Ginés Moreno ◽  
Jaime Penabad ◽  
José A. Riaza ◽  
Germán Vidal
Keyword(s):  
Fuzzy Logic ◽  
Symbolic Execution ◽  
Logic Programs

Connectionist weighted fuzzy logic programs

2008 ◽  
Vol 71 (13-15) ◽  
pp. 2456-2469
Author(s):  
Alexandros Chortaras ◽  
Giorgos Stamou ◽  
Andreas Stafylopatis
Keyword(s):  
Fuzzy Logic ◽  
Logic Programs

scholarly journals f-DLPs: Extending Description Logic Programs with Fuzzy Sets and Fuzzy Logic

2007 ◽  
Author(s):  
Tassos Venetis ◽  
Giorgos Stoilos ◽  
Giorgos Stamou ◽  
Stefanos Kollias
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Description Logic ◽  
Logic Programs

scholarly journals Unfolding-based Improvements on Fuzzy Logic Programs

2005 ◽  
Vol 137 (1) ◽  
pp. 69-103
Author(s):  
Pascual Julián ◽  
Ginés Moreno ◽  
Jaime Penabad
Keyword(s):  
Fuzzy Logic ◽  
Logic Programs

scholarly journals Optimizing Fuzzy Logic Programs by Unfolding, Aggregation and Folding

2008 ◽  
Vol 219 ◽  
pp. 19-34 ◽  
Author(s):  
Juan Antonio Guerrero ◽  
Ginés Moreno
Keyword(s):  
Fuzzy Logic ◽  
Logic Programs

scholarly journals Fuzzy Reasoning with Tunable t-Operators

1998 ◽  
Vol 2 (4) ◽  
pp. 121-127 ◽  
Author(s):  
Peter Vojtig ◽  
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Normal Form ◽  
Fuzzy Controller ◽  
Fuzzy Reasoning ◽  
Disjunctive Normal Form ◽  
Logic Programs ◽  
Real World Problem ◽  
The Subject ◽  
Soundness And Completeness

We introduce a model of fuzzy logic programming in a truth functional fuzzy logic with arbitrary and/or tunable t-operators. This t-operator tuning is the subject of different learning from neural networks to evolutionary calculation. The choice of an operator mostly depends on the real world problem modeled, often depending on user environments and/or stereotypes. To model aggregations of different witnesses, our rules have body in disjunctive normal form. We develop fuzzy fixpoint theory and show soundness and completeness of our semantics. To control calculational efficiency, we introduce a cut with threshold. For knowledge mining and tuning of the t-operator, we restrict the problem to finding a tnorm fitting finitely many values. We show that our model of fuzzy logic programs semantically coincides with a fuzzy controller model.

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