A generalization of the homogenization process needed for the neural implementation of multi-adjoint logic programming (a unifying theory to deal with uncertainty, imprecise data or incomplete information) is presented here. The idea is to allow to represent a more general family of adjoint pairs, but maintaining the advantage of the existing implementation recently introduced in [6]. The soundness of the transformation is proved and its complexity is analysed. In addition, the corresponding generalization of the neural-like implementation of the fixed point semantics of multi-adjoint is presented.
@article{urn:eudml:doc:40868,
title = {A neural implementation of multi-adjoint logic programs via sf-homogenization.},
journal = {Mathware and Soft Computing},
volume = {12},
year = {2005},
pages = {199-216},
zbl = {1103.68032},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:40868}
}
Medina, Jesús; Mérida-Casermeiro, Enrique; Ojeda-Aciego, Manuel. A neural implementation of multi-adjoint logic programs via sf-homogenization.. Mathware and Soft Computing, Tome 12 (2005) pp. 199-216. http://gdmltest.u-ga.fr/item/urn:eudml:doc:40868/