References in periodicals archive ?
Operational Laws on NFNs. The operational laws that we use are based on triangular norms (t-norms and t-conorms).
(2010) ], we introduce below three new operational laws on NFNs, which will be very useful in the sequel of this paper.
Let [mathematical expression not reproducible] be two trapezoidal NFNs (TpNFNs).
Nevertheless, if the weighting vector is presented with NFNs, then, instead of converting these fuzzy weights into representative exact numbers by using a method for doing so as recommended by some authors, our recommendation is, for example, to use the fuzzy weighting vector model defined as follows: let [direct sum] be a t-conorm.
NFNS syndrome was suspected, and cranial MRI was performed to evaluate the neurological involvement.
Genetic analyses were performed for the patient and her father to investigate NFNS and the genetic analysis of both the patient and the father revealed a truncating mutation c.7846C>T (M82814), p.Arg2616X (AAA59924) in the NF1 gene.
NFNS is an entity presenting with clinical characteristics of both NF1 and NS.
The genetic studies that have been undertaken to identify the gene causing NFNS have shown that the majority of these cases have a mutation in the NF1 gene.
After reviewing of training algorithms for NFNs in Section 2, Section 3 illustrates the structure of the TSK-type fuzzy model.
Besides the most-applied BP algorithm, some other traditional optimization approaches had been applied to training NFNs, such as the Broyden-Fletcher-Goldfarb-Shanno (BFGS) -, conjugate gradient (CG) -, and Levenberg-Marquardt (LM) - methods.
Evolutional approaches such as particle swarm optimization (PSO) , differential evolution (DE) , and symbiotic evolution (SE)  have been developed for training NFNs -, respectively.
Acronyms browser ?
Full browser ?